{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this notebook data from MarkerDB database related to chemical diagnostic markers-disease  associations is processed, inserted into SQL tables and analysed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Operations with data from Markerdb database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker_id</th>\n",
       "      <th>marker</th>\n",
       "      <th>name_id</th>\n",
       "      <th>name</th>\n",
       "      <th>patient</th>\n",
       "      <th>gender</th>\n",
       "      <th>sample</th>\n",
       "      <th>concentration</th>\n",
       "      <th>Column9</th>\n",
       "      <th>reference</th>\n",
       "      <th></th>\n",
       "      <th>Column12</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuma, P., Samcova, E. &amp; Balinova, P. Determina...</td>\n",
       "      <td>15899597.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>85.8 (17.7-153.8) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>David F. Putnam Composition and Concentrative ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>33</td>\n",
       "      <td>Alzheimer's Disease</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>15.7 (11.7-19.7) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fonteh, A. N., Harrington, R. J., Tsai, A., Li...</td>\n",
       "      <td>17031479.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>34</td>\n",
       "      <td>Pregnancy</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Female</td>\n",
       "      <td>Blood</td>\n",
       "      <td>50.0 uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22494326.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Blood</td>\n",
       "      <td>12.7 uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dohm, G. L., Williams, R. T., Kasperek, G. J. ...</td>\n",
       "      <td>7061274.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>4961</td>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>0.0023 (0.00090-0.0058) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24023812.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>4961</td>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>34</td>\n",
       "      <td>Pregnancy</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Female</td>\n",
       "      <td>Blood</td>\n",
       "      <td>4.4 uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24704061.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3391</th>\n",
       "      <td>4961</td>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Blood</td>\n",
       "      <td>15.4 uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Molecular You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>4973</td>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>6245</td>\n",
       "      <td>Vertebral Osteoporosis</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>&gt;63 nmol/mmol creatine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seibel, M. J., Woitge, H., Scheidt-Nave, C., L...</td>\n",
       "      <td>7817828.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>4973</td>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>&lt;63 nmol/mmol creatine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3394 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      marker_id                                     marker  name_id  \\\n",
       "0             1                          1-Methylhistidine      251   \n",
       "1             1                          1-Methylhistidine        1   \n",
       "2             1                          1-Methylhistidine       33   \n",
       "3             1                          1-Methylhistidine       34   \n",
       "4             1                          1-Methylhistidine        1   \n",
       "...         ...                                        ...      ...   \n",
       "3389       4961                    SM(d18:0/22:1(13Z)(OH))        1   \n",
       "3390       4961                    SM(d18:0/22:1(13Z)(OH))       34   \n",
       "3391       4961                    SM(d18:0/22:1(13Z)(OH))        1   \n",
       "3392       4973  Carboxyterminal telopeptide of collagen 1     6245   \n",
       "3393       4973  Carboxyterminal telopeptide of collagen 1        1   \n",
       "\n",
       "                        name              patient  gender sample  \\\n",
       "0                    Obesity  Adult: >=18 yrs old    Both  Urine   \n",
       "1                     Normal  Adult: >=18 yrs old    Both  Urine   \n",
       "2        Alzheimer's Disease  Adult: >=18 yrs old    Both  Urine   \n",
       "3                  Pregnancy  Adult: >=18 yrs old  Female  Blood   \n",
       "4                     Normal  Adult: >=18 yrs old    Both  Blood   \n",
       "...                      ...                  ...     ...    ...   \n",
       "3389                  Normal  Adult: >=18 yrs old    Both  Urine   \n",
       "3390               Pregnancy  Adult: >=18 yrs old  Female  Blood   \n",
       "3391                  Normal  Adult: >=18 yrs old    Both  Blood   \n",
       "3392  Vertebral Osteoporosis  Adult: >=18 yrs old    Both  Urine   \n",
       "3393                  Normal  Adult: >=18 yrs old    Both  Urine   \n",
       "\n",
       "                                     concentration  Column9  \\\n",
       "0            10.9 (0.80-21.0) umol/mmol creatinine      NaN   \n",
       "1           85.8 (17.7-153.8) umol/mmol creatinine      NaN   \n",
       "2            15.7 (11.7-19.7) umol/mmol creatinine      NaN   \n",
       "3                                          50.0 uM      NaN   \n",
       "4                                          12.7 uM      NaN   \n",
       "...                                            ...      ...   \n",
       "3389  0.0023 (0.00090-0.0058) umol/mmol creatinine      NaN   \n",
       "3390                                        4.4 uM      NaN   \n",
       "3391                                       15.4 uM      NaN   \n",
       "3392                        >63 nmol/mmol creatine      NaN   \n",
       "3393                        <63 nmol/mmol creatine      NaN   \n",
       "\n",
       "                                              reference              Column12  \\\n",
       "0     Tuma, P., Samcova, E. & Balinova, P. Determina...  15899597.0         2   \n",
       "1     David F. Putnam Composition and Concentrative ...         NaN         2   \n",
       "2     Fonteh, A. N., Harrington, R. J., Tsai, A., Li...  17031479.0         2   \n",
       "3                                                   NaN  22494326.0         2   \n",
       "4     Dohm, G. L., Williams, R. T., Kasperek, G. J. ...   7061274.0         2   \n",
       "...                                                 ...         ...       ...   \n",
       "3389                                                NaN  24023812.0         2   \n",
       "3390                                                NaN  24704061.0         2   \n",
       "3391                                      Molecular You         NaN         2   \n",
       "3392  Seibel, M. J., Woitge, H., Scheidt-Nave, C., L...   7817828.0         2   \n",
       "3393                                                <63         NaN         2   \n",
       "\n",
       "            type  \n",
       "0     Diagnostic  \n",
       "1     Diagnostic  \n",
       "2     Diagnostic  \n",
       "3     Diagnostic  \n",
       "4     Diagnostic  \n",
       "...          ...  \n",
       "3389  Diagnostic  \n",
       "3390  Diagnostic  \n",
       "3391  Diagnostic  \n",
       "3392  Diagnostic  \n",
       "3393  Diagnostic  \n",
       "\n",
       "[3394 rows x 13 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the data containing the associations between chemical diagnostic markers and diseases.\n",
    "df1 = pd.read_excel(\"all_diagnostic_chemical_markers.xlsx\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker_id</th>\n",
       "      <th>marker</th>\n",
       "      <th>name_id</th>\n",
       "      <th>name</th>\n",
       "      <th>patient</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuma, P., Samcova, E. &amp; Balinova, P. Determina...</td>\n",
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       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>33</td>\n",
       "      <td>Alzheimer's Disease</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>15.7 (11.7-19.7) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fonteh, A. N., Harrington, R. J., Tsai, A., Li...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>5988</td>\n",
       "      <td>Preeclampsia/Eclampsia</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Female</td>\n",
       "      <td>Blood</td>\n",
       "      <td>50.7 uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22494326.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>83</td>\n",
       "      <td>Chronic Kidney Disease</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Blood</td>\n",
       "      <td>28.8 (10.6-47.0) uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Raj, D. S., Ouwendyk, M., Francoeur, R. &amp; Pier...</td>\n",
       "      <td>10838467.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1,3-Diaminopropane</td>\n",
       "      <td>207</td>\n",
       "      <td>Leukemia</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>0.96 (0.12-2.1) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lee, S. H., Suh, J. W., Chung, B. C. &amp; Kim, S....</td>\n",
       "      <td>9464484.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>4931</td>\n",
       "      <td>PC(o-18:1(11Z)/18:2(9Z,12Z))</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Children: 2-17 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Blood</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24740590.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>4934</td>\n",
       "      <td>PC(o-18:1(9Z)/18:2(9Z,12Z))</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Children: 2-17 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Blood</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24740590.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>4957</td>\n",
       "      <td>SM(d18:0/14:1(9Z)(OH))</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Adolescent:13-18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>0.36 (0.32-0.40) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26910390.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>4961</td>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>251</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>Adolescent:13-18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>2.2 (2.1-2.3) umol/mmol creatinine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26910390.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>4973</td>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>6245</td>\n",
       "      <td>Vertebral Osteoporosis</td>\n",
       "      <td>Adult: &gt;=18 yrs old</td>\n",
       "      <td>Both</td>\n",
       "      <td>Urine</td>\n",
       "      <td>&gt;63 nmol/mmol creatine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seibel, M. J., Woitge, H., Scheidt-Nave, C., L...</td>\n",
       "      <td>7817828.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Diagnostic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      marker_id                                     marker  name_id  \\\n",
       "0             1                          1-Methylhistidine      251   \n",
       "2             1                          1-Methylhistidine       33   \n",
       "5             1                          1-Methylhistidine     5988   \n",
       "6             1                          1-Methylhistidine       83   \n",
       "7             2                         1,3-Diaminopropane      207   \n",
       "...         ...                                        ...      ...   \n",
       "3356       4931               PC(o-18:1(11Z)/18:2(9Z,12Z))      251   \n",
       "3358       4934                PC(o-18:1(9Z)/18:2(9Z,12Z))      251   \n",
       "3386       4957                     SM(d18:0/14:1(9Z)(OH))      251   \n",
       "3388       4961                    SM(d18:0/22:1(13Z)(OH))      251   \n",
       "3392       4973  Carboxyterminal telopeptide of collagen 1     6245   \n",
       "\n",
       "                        name                   patient  gender sample  \\\n",
       "0                    Obesity       Adult: >=18 yrs old    Both  Urine   \n",
       "2        Alzheimer's Disease       Adult: >=18 yrs old    Both  Urine   \n",
       "5     Preeclampsia/Eclampsia       Adult: >=18 yrs old  Female  Blood   \n",
       "6     Chronic Kidney Disease       Adult: >=18 yrs old    Both  Blood   \n",
       "7                   Leukemia       Adult: >=18 yrs old    Both  Urine   \n",
       "...                      ...                       ...     ...    ...   \n",
       "3356                 Obesity    Children: 2-17 yrs old    Both  Blood   \n",
       "3358                 Obesity    Children: 2-17 yrs old    Both  Blood   \n",
       "3386                 Obesity  Adolescent:13-18 yrs old    Both  Urine   \n",
       "3388                 Obesity  Adolescent:13-18 yrs old    Both  Urine   \n",
       "3392  Vertebral Osteoporosis       Adult: >=18 yrs old    Both  Urine   \n",
       "\n",
       "                              concentration  Column9  \\\n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine      NaN   \n",
       "2     15.7 (11.7-19.7) umol/mmol creatinine      NaN   \n",
       "5                                   50.7 uM      NaN   \n",
       "6                       28.8 (10.6-47.0) uM      NaN   \n",
       "7      0.96 (0.12-2.1) umol/mmol creatinine      NaN   \n",
       "...                                     ...      ...   \n",
       "3356                      8.4 (5.7-11.1) uM      NaN   \n",
       "3358                      8.4 (5.7-11.1) uM      NaN   \n",
       "3386  0.36 (0.32-0.40) umol/mmol creatinine      NaN   \n",
       "3388     2.2 (2.1-2.3) umol/mmol creatinine      NaN   \n",
       "3392                 >63 nmol/mmol creatine      NaN   \n",
       "\n",
       "                                              reference              Column12  \\\n",
       "0     Tuma, P., Samcova, E. & Balinova, P. Determina...  15899597.0         2   \n",
       "2     Fonteh, A. N., Harrington, R. J., Tsai, A., Li...  17031479.0         2   \n",
       "5                                                   NaN  22494326.0         2   \n",
       "6     Raj, D. S., Ouwendyk, M., Francoeur, R. & Pier...  10838467.0         2   \n",
       "7     Lee, S. H., Suh, J. W., Chung, B. C. & Kim, S....   9464484.0         2   \n",
       "...                                                 ...         ...       ...   \n",
       "3356                                                NaN  24740590.0         2   \n",
       "3358                                                NaN  24740590.0         2   \n",
       "3386                                                NaN  26910390.0         2   \n",
       "3388                                                NaN  26910390.0         2   \n",
       "3392  Seibel, M. J., Woitge, H., Scheidt-Nave, C., L...   7817828.0         2   \n",
       "\n",
       "            type  \n",
       "0     Diagnostic  \n",
       "2     Diagnostic  \n",
       "5     Diagnostic  \n",
       "6     Diagnostic  \n",
       "7     Diagnostic  \n",
       "...          ...  \n",
       "3356  Diagnostic  \n",
       "3358  Diagnostic  \n",
       "3386  Diagnostic  \n",
       "3388  Diagnostic  \n",
       "3392  Diagnostic  \n",
       "\n",
       "[1545 rows x 13 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df1[df1.name != \"Normal\"]#discard of normal (not pathologic) marker concentrations.\n",
    "df2= df2[df2.name != \"Pregnancy\"]#discard of pregnancy as it is not a disease (marker associated to a pregnancy situation)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Alzheimer's Disease</td>\n",
       "      <td>15.7 (11.7-19.7) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Preeclampsia/Eclampsia</td>\n",
       "      <td>50.7 uM</td>\n",
       "      <td>Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Chronic Kidney Disease</td>\n",
       "      <td>28.8 (10.6-47.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,3-Diaminopropane</td>\n",
       "      <td>Leukemia</td>\n",
       "      <td>0.96 (0.12-2.1) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>PC(o-18:1(11Z)/18:2(9Z,12Z))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>PC(o-18:1(9Z)/18:2(9Z,12Z))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>SM(d18:0/14:1(9Z)(OH))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>0.36 (0.32-0.40) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>2.2 (2.1-2.3) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>Vertebral Osteoporosis</td>\n",
       "      <td>&gt;63 nmol/mmol creatine</td>\n",
       "      <td>Urine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1545 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         marker                    name  \\\n",
       "0                             1-Methylhistidine                 Obesity   \n",
       "2                             1-Methylhistidine     Alzheimer's Disease   \n",
       "5                             1-Methylhistidine  Preeclampsia/Eclampsia   \n",
       "6                             1-Methylhistidine  Chronic Kidney Disease   \n",
       "7                            1,3-Diaminopropane                Leukemia   \n",
       "...                                         ...                     ...   \n",
       "3356               PC(o-18:1(11Z)/18:2(9Z,12Z))                 Obesity   \n",
       "3358                PC(o-18:1(9Z)/18:2(9Z,12Z))                 Obesity   \n",
       "3386                     SM(d18:0/14:1(9Z)(OH))                 Obesity   \n",
       "3388                    SM(d18:0/22:1(13Z)(OH))                 Obesity   \n",
       "3392  Carboxyterminal telopeptide of collagen 1  Vertebral Osteoporosis   \n",
       "\n",
       "                              concentration sample  \n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine  \n",
       "2     15.7 (11.7-19.7) umol/mmol creatinine  Urine  \n",
       "5                                   50.7 uM  Blood  \n",
       "6                       28.8 (10.6-47.0) uM  Blood  \n",
       "7      0.96 (0.12-2.1) umol/mmol creatinine  Urine  \n",
       "...                                     ...    ...  \n",
       "3356                      8.4 (5.7-11.1) uM  Blood  \n",
       "3358                      8.4 (5.7-11.1) uM  Blood  \n",
       "3386  0.36 (0.32-0.40) umol/mmol creatinine  Urine  \n",
       "3388     2.2 (2.1-2.3) umol/mmol creatinine  Urine  \n",
       "3392                 >63 nmol/mmol creatine  Urine  \n",
       "\n",
       "[1545 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[['marker', 'name', 'concentration', 'sample']]#selecting the fields we are interested to according to our database schema\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2.to_excel('marker_chem_diag.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-d717a880b5a9>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['source_id'] = 7\n",
      "<ipython-input-70-d717a880b5a9>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['source_name'] = \"Markerdb\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "      <th>source_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Alzheimer's Disease</td>\n",
       "      <td>15.7 (11.7-19.7) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Preeclampsia/Eclampsia</td>\n",
       "      <td>50.7 uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>Chronic Kidney Disease</td>\n",
       "      <td>28.8 (10.6-47.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,3-Diaminopropane</td>\n",
       "      <td>Leukemia</td>\n",
       "      <td>0.96 (0.12-2.1) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>PC(o-18:1(11Z)/18:2(9Z,12Z))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>PC(o-18:1(9Z)/18:2(9Z,12Z))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>SM(d18:0/14:1(9Z)(OH))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>0.36 (0.32-0.40) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>2.2 (2.1-2.3) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>Vertebral Osteoporosis</td>\n",
       "      <td>&gt;63 nmol/mmol creatine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         marker                    name  \\\n",
       "0                             1-Methylhistidine                 Obesity   \n",
       "2                             1-Methylhistidine     Alzheimer's Disease   \n",
       "5                             1-Methylhistidine  Preeclampsia/Eclampsia   \n",
       "6                             1-Methylhistidine  Chronic Kidney Disease   \n",
       "7                            1,3-Diaminopropane                Leukemia   \n",
       "...                                         ...                     ...   \n",
       "3356               PC(o-18:1(11Z)/18:2(9Z,12Z))                 Obesity   \n",
       "3358                PC(o-18:1(9Z)/18:2(9Z,12Z))                 Obesity   \n",
       "3386                     SM(d18:0/14:1(9Z)(OH))                 Obesity   \n",
       "3388                    SM(d18:0/22:1(13Z)(OH))                 Obesity   \n",
       "3392  Carboxyterminal telopeptide of collagen 1  Vertebral Osteoporosis   \n",
       "\n",
       "                              concentration sample  source_id source_name  \n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "2     15.7 (11.7-19.7) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "5                                   50.7 uM  Blood          7    Markerdb  \n",
       "6                       28.8 (10.6-47.0) uM  Blood          7    Markerdb  \n",
       "7      0.96 (0.12-2.1) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "...                                     ...    ...        ...         ...  \n",
       "3356                      8.4 (5.7-11.1) uM  Blood          7    Markerdb  \n",
       "3358                      8.4 (5.7-11.1) uM  Blood          7    Markerdb  \n",
       "3386  0.36 (0.32-0.40) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "3388     2.2 (2.1-2.3) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "3392                 >63 nmol/mmol creatine  Urine          7    Markerdb  \n",
       "\n",
       "[1545 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#addition of columns source_id and source_name. This specifies where these disease-marker associations are from\n",
    "df2['source_id'] = 7\n",
    "df2['source_name'] = \"Markerdb\"\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urine', 'Blood', 'Cerebrospinal_Fluid', 'Cellular_Cytoplasm',\n",
       "       'Saliva', 'Feces', 'Sweat'], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['sample'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['name'].nunique()#counting the different diseases Markerdb has associated to diagnostic chemical markers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparison between DISNET and Markerdb in terms of shared diseases among them**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "import json\n",
    "conn_param_file = 'C:/Users/end user/OneDrive/Desktop/UPM Master/CTB TFM/Datasets/DISNET_CONNECTION_correct.json'\n",
    "# Setting the connection to DISNET database\n",
    "\n",
    "# The connection configuration is stored in a JSON file\n",
    "with open(conn_param_file, 'r') as f:\n",
    "    config = json.load(f) # The JSON file is translated to a pyhton dictionary\n",
    "\n",
    "# Stablishing the connection with the parameters in the dictionary\n",
    "cnx = mysql.connector.connect(**config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-74-4efa9d04dac3>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df3['name'] = df2['name'].str.lower()#the convertion of column 'name' to lower cases is done because\n"
     ]
    }
   ],
   "source": [
    "df3 = df2\n",
    "df3['name'] = df2['name'].str.lower()#the convertion of column 'name' to lower cases is done because\n",
    "#thanks to previous operations, we found that many disease names from \"all_diagnostic_chemical_markers.xlsx\"\n",
    "#dataset match with disease names from DISNET when terms from both sides are transformed into lower cases\n",
    "#This allows to capture more diseases to integrate them in DISNET's biological layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "      <th>source_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>alzheimer's disease</td>\n",
       "      <td>15.7 (11.7-19.7) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>preeclampsia/eclampsia</td>\n",
       "      <td>50.7 uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>chronic kidney disease</td>\n",
       "      <td>28.8 (10.6-47.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,3-Diaminopropane</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>0.96 (0.12-2.1) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>PC(o-18:1(11Z)/18:2(9Z,12Z))</td>\n",
       "      <td>obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>PC(o-18:1(9Z)/18:2(9Z,12Z))</td>\n",
       "      <td>obesity</td>\n",
       "      <td>8.4 (5.7-11.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>SM(d18:0/14:1(9Z)(OH))</td>\n",
       "      <td>obesity</td>\n",
       "      <td>0.36 (0.32-0.40) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>SM(d18:0/22:1(13Z)(OH))</td>\n",
       "      <td>obesity</td>\n",
       "      <td>2.2 (2.1-2.3) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>Carboxyterminal telopeptide of collagen 1</td>\n",
       "      <td>vertebral osteoporosis</td>\n",
       "      <td>&gt;63 nmol/mmol creatine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         marker                    name  \\\n",
       "0                             1-Methylhistidine                 obesity   \n",
       "2                             1-Methylhistidine     alzheimer's disease   \n",
       "5                             1-Methylhistidine  preeclampsia/eclampsia   \n",
       "6                             1-Methylhistidine  chronic kidney disease   \n",
       "7                            1,3-Diaminopropane                leukemia   \n",
       "...                                         ...                     ...   \n",
       "3356               PC(o-18:1(11Z)/18:2(9Z,12Z))                 obesity   \n",
       "3358                PC(o-18:1(9Z)/18:2(9Z,12Z))                 obesity   \n",
       "3386                     SM(d18:0/14:1(9Z)(OH))                 obesity   \n",
       "3388                    SM(d18:0/22:1(13Z)(OH))                 obesity   \n",
       "3392  Carboxyterminal telopeptide of collagen 1  vertebral osteoporosis   \n",
       "\n",
       "                              concentration sample  source_id source_name  \n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "2     15.7 (11.7-19.7) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "5                                   50.7 uM  Blood          7    Markerdb  \n",
       "6                       28.8 (10.6-47.0) uM  Blood          7    Markerdb  \n",
       "7      0.96 (0.12-2.1) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "...                                     ...    ...        ...         ...  \n",
       "3356                      8.4 (5.7-11.1) uM  Blood          7    Markerdb  \n",
       "3358                      8.4 (5.7-11.1) uM  Blood          7    Markerdb  \n",
       "3386  0.36 (0.32-0.40) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "3388     2.2 (2.1-2.3) umol/mmol creatinine  Urine          7    Markerdb  \n",
       "3392                 >63 nmol/mmol creatine  Urine          7    Markerdb  \n",
       "\n",
       "[1545 rows x 6 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_enf = df3['name']#capturing disease names from all_diagnostic_chemical_markers.xlsx dataset converted into lower cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_marker = list(df3_enf.drop_duplicates())#list containing all the diseases from 'all_diagnostic_chemical_markers.xlsx' dataset\n",
    "#list_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>name</th>\n",
       "      <th>vocab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0000735</td>\n",
       "      <td>Abdominal Neoplasms</td>\n",
       "      <td>Abdominal Neoplasms</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0000737</td>\n",
       "      <td>Abdominal Pain</td>\n",
       "      <td>Colicky Pain</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>Abetalipoproteinemia</td>\n",
       "      <td>abetalipoproteinemia</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>Abetalipoproteinemia</td>\n",
       "      <td>Abetalipoproteinemia</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>Abetalipoproteinemia</td>\n",
       "      <td>hypolipoproteinemia</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22229</th>\n",
       "      <td>C4540400</td>\n",
       "      <td>SPINOCEREBELLAR ATAXIA 45</td>\n",
       "      <td>spinocerebellar ataxia 45</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22230</th>\n",
       "      <td>C4540404</td>\n",
       "      <td>SPINOCEREBELLAR ATAXIA 46</td>\n",
       "      <td>spinocerebellar ataxia 46</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22231</th>\n",
       "      <td>C4540411</td>\n",
       "      <td>EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 57</td>\n",
       "      <td>early infantile epileptic encephalopathy 57</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22232</th>\n",
       "      <td>C4540470</td>\n",
       "      <td>MENTAL RETARDATION, AUTOSOMAL DOMINANT 50</td>\n",
       "      <td>autosomal dominant mental retardation 50</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22233</th>\n",
       "      <td>C4540499</td>\n",
       "      <td>COFFIN-SIRIS SYNDROME 6</td>\n",
       "      <td>Coffin-Siris syndrome 6</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22234 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      disease_id                                   disease_name  \\\n",
       "0       C0000735                            Abdominal Neoplasms   \n",
       "1       C0000737                                 Abdominal Pain   \n",
       "2       C0000744                           Abetalipoproteinemia   \n",
       "3       C0000744                           Abetalipoproteinemia   \n",
       "4       C0000744                           Abetalipoproteinemia   \n",
       "...          ...                                            ...   \n",
       "22229   C4540400                      SPINOCEREBELLAR ATAXIA 45   \n",
       "22230   C4540404                      SPINOCEREBELLAR ATAXIA 46   \n",
       "22231   C4540411  EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 57   \n",
       "22232   C4540470      MENTAL RETARDATION, AUTOSOMAL DOMINANT 50   \n",
       "22233   C4540499                        COFFIN-SIRIS SYNDROME 6   \n",
       "\n",
       "                                              name vocab  \n",
       "0                              Abdominal Neoplasms   MSH  \n",
       "1                                     Colicky Pain   MSH  \n",
       "2                             abetalipoproteinemia    DO  \n",
       "3                             Abetalipoproteinemia   MSH  \n",
       "4                              hypolipoproteinemia    DO  \n",
       "...                                            ...   ...  \n",
       "22229                    spinocerebellar ataxia 45    DO  \n",
       "22230                    spinocerebellar ataxia 46    DO  \n",
       "22231  early infantile epileptic encephalopathy 57    DO  \n",
       "22232     autosomal dominant mental retardation 50    DO  \n",
       "22233                      Coffin-Siris syndrome 6    DO  \n",
       "\n",
       "[22234 rows x 4 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Querying DISNET database to obtain in a dataframe object the CUI codes from the diseases associated to RNAs.\n",
    "#Diseases that make up the mentioned associations are in MeSH terminology or in Disease Ontology (DO) terminology.\n",
    "\n",
    "disnet_cui_name = \"\"\"\n",
    "                    SELECT DISTINCT\n",
    "                        d.disease_id,\n",
    "                        d.disease_name,\n",
    "                        c.name AS name,\n",
    "                        c.vocabulary AS vocab\n",
    "                    FROM\n",
    "                        disnet_biolayer.code c\n",
    "                            INNER JOIN\n",
    "                        disnet_biolayer.has_code hc ON c.code_id = hc.code_id\n",
    "                            AND hc.vocabulary = c.vocabulary\n",
    "                            INNER JOIN\n",
    "                        disnet_biolayer.disease d ON hc.disease_id = d.disease_id\n",
    "                    WHERE\n",
    "                        c.vocabulary =  'DO' OR c.vocabulary = 'MSH';\n",
    "                        \"\"\"\n",
    "disnet_cui_name = pd.read_sql_query(disnet_cui_name, con = cnx)\n",
    "disnet_cui_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "disnet_name = disnet_cui_name['name'].str.lower()#converting into lower cases the disease names in MeSH and DO terminology\n",
    "list_disnet1 = list(disnet_name.drop_duplicates())#convert into list 'disnet_name' dataframe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detecting diseases from Markerdb that appear in DISNET database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list comparison. \n",
    "no_disnet = [] #stores the diseases from \"Markerdb\" (stored in list_marker) that DO NOT match with DISNET ones.\n",
    "si_disnet = [] #stores the diseases from \"Markerdb\" (stored in list_marker) that DO match with DISNET ones.\n",
    "for i in list_marker:\n",
    "    if i in list_disnet1:\n",
    "        si_disnet.append(i)\n",
    "        #continue\n",
    "    else:\n",
    "        no_disnet.append(i)\n",
    "        #no_disnet.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "157\n",
      "136\n"
     ]
    }
   ],
   "source": [
    "print(len(list_marker))\n",
    "print(len(no_disnet))\n",
    "print(len(si_disnet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From 293 different diseases from Markerdb database contained in all_diagnostic_chemical_markers.xlsx dataset, 136 match with diseases from DISNET in MeSH or Disease Ontology (DO) terminology**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging Markerdb with DISNET**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_marker_db = df3 #df3 (dataframe containing the information related to markers,\n",
    "#disease ids, marker concentrations, samples and source ids is renamed to df_marker_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "disnet_cui_name['name'] = disnet_cui_name['name'].str.lower()#converting into lower cases the disease names from DISNET that are in MeSH and DO\n",
    "#In disnet_cui_name dataframe these are stored in column \"name\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "      <th>source_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>vocab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0039870</td>\n",
       "      <td>Thinness</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0451819</td>\n",
       "      <td>Simple obesity</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>obesity</td>\n",
       "      <td>235.0 (218.0-252.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>Trihexosylceramide (d18:1/26:1(17Z))</td>\n",
       "      <td>hypobetalipoproteinemia</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>Hypobetalipoproteinemias</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>Trihexosylceramide (d18:1/24:0)</td>\n",
       "      <td>hypobetalipoproteinemia</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>Hypobetalipoproteinemias</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3294</th>\n",
       "      <td>Tetrahexosylceramide (d18:1/12:0)</td>\n",
       "      <td>hypobetalipoproteinemia</td>\n",
       "      <td>1.00 (1.00-1.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>Hypobetalipoproteinemias</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>5-HETE</td>\n",
       "      <td>rhinitis</td>\n",
       "      <td>0.79 (0.73-0.84) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0035455</td>\n",
       "      <td>Rhinitis</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3296</th>\n",
       "      <td>5-HETE</td>\n",
       "      <td>rhinitis</td>\n",
       "      <td>0.79 (0.73-0.84) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0035455</td>\n",
       "      <td>Rhinitis</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3297 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    marker                     name  \\\n",
       "0                        1-Methylhistidine                  obesity   \n",
       "1                        1-Methylhistidine                  obesity   \n",
       "2                        1-Methylhistidine                  obesity   \n",
       "3                        1-Methylhistidine                  obesity   \n",
       "4                (R)-3-Hydroxybutyric acid                  obesity   \n",
       "...                                    ...                      ...   \n",
       "3292  Trihexosylceramide (d18:1/26:1(17Z))  hypobetalipoproteinemia   \n",
       "3293       Trihexosylceramide (d18:1/24:0)  hypobetalipoproteinemia   \n",
       "3294     Tetrahexosylceramide (d18:1/12:0)  hypobetalipoproteinemia   \n",
       "3295                                5-HETE                 rhinitis   \n",
       "3296                                5-HETE                 rhinitis   \n",
       "\n",
       "                              concentration sample  source_id source_name  \\\n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb   \n",
       "1     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb   \n",
       "2     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb   \n",
       "3     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7    Markerdb   \n",
       "4                    235.0 (218.0-252.0) uM  Blood          7    Markerdb   \n",
       "...                                     ...    ...        ...         ...   \n",
       "3292                    0.90 (0.90-1.00) uM  Blood          7    Markerdb   \n",
       "3293                    0.90 (0.90-1.00) uM  Blood          7    Markerdb   \n",
       "3294                     1.00 (1.00-1.1) uM  Blood          7    Markerdb   \n",
       "3295                    0.79 (0.73-0.84) uM  Blood          7    Markerdb   \n",
       "3296                    0.79 (0.73-0.84) uM  Blood          7    Markerdb   \n",
       "\n",
       "     disease_id              disease_name vocab  \n",
       "0      C0028754                   Obesity    DO  \n",
       "1      C0028754                   Obesity   MSH  \n",
       "2      C0039870                  Thinness    DO  \n",
       "3      C0451819            Simple obesity    DO  \n",
       "4      C0028754                   Obesity    DO  \n",
       "...         ...                       ...   ...  \n",
       "3292   C0020597  Hypobetalipoproteinemias    DO  \n",
       "3293   C0020597  Hypobetalipoproteinemias    DO  \n",
       "3294   C0020597  Hypobetalipoproteinemias    DO  \n",
       "3295   C0035455                  Rhinitis    DO  \n",
       "3296   C0035455                  Rhinitis   MSH  \n",
       "\n",
       "[3297 rows x 9 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging dataframes from Markerdb and Disnet through the field name (disease names in MeSH and DO terminology shared by both information sources)\n",
    "markerdb_disnet = pd.merge(df_marker_db, disnet_cui_name, on='name')\n",
    "markerdb_disnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "341"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique diseases\n",
    "markerdb_disnet['disease_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marker-disease data insertion into DISNET biolayer in my local host"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Connection to my local host and markerdatabase**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First I will provide my connection details to my local host.\n",
    "#In my local host, I have already created the tables (disease_marker for this case) to fill with the information \n",
    "#from the associations between chemical diagnostic markers and diseases.\n",
    "#Table disease_marker is part of the schema disnet_biolayer_expansion where there is information about RNAs, diseases,\n",
    "#disease-RNA associations and more features.\n",
    "\n",
    "conn_param_file_pablo = 'C:/Users/end user/OneDrive/Desktop/UPM Master/CTB TFM/Datasets/Pablo_Database_connection_disnet_biolayer_2.json'\n",
    "# Setting the connection to the database disnet_biolayer\n",
    "\n",
    "# The connection configuration is stored in a JSON file\n",
    "with open(conn_param_file_pablo, 'r') as f:\n",
    "    config = json.load(f) # The JSON file is translated to a pyhton dictionary\n",
    "#print(config)\n",
    "# Stablishing the connection with the parameters in the dictionary\n",
    "cnx = mysql.connector.connect(**config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mysql.connector.connection_cext.CMySQLConnection at 0x29ab9400bb0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['marker', 'name', 'concentration', 'sample', 'source_id', 'source_name',\n",
       "       'disease_id', 'disease_name', 'vocab'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markerdb_disnet.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preparing the data of interest from Markerdb to be inserted in DISNET**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0039870</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0451819</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>235.0 (218.0-252.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>C0039870</td>\n",
       "      <td>235.0 (218.0-252.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3291</th>\n",
       "      <td>Trihexosylceramide (d18:1/22:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (1.00-0.90) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>Trihexosylceramide (d18:1/26:1(17Z))</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>Trihexosylceramide (d18:1/24:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3294</th>\n",
       "      <td>Tetrahexosylceramide (d18:1/12:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>1.00 (1.00-1.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>5-HETE</td>\n",
       "      <td>C0035455</td>\n",
       "      <td>0.79 (0.73-0.84) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2763 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    marker disease_id  \\\n",
       "0                        1-Methylhistidine   C0028754   \n",
       "2                        1-Methylhistidine   C0039870   \n",
       "3                        1-Methylhistidine   C0451819   \n",
       "4                (R)-3-Hydroxybutyric acid   C0028754   \n",
       "6                (R)-3-Hydroxybutyric acid   C0039870   \n",
       "...                                    ...        ...   \n",
       "3291       Trihexosylceramide (d18:1/22:0)   C0020597   \n",
       "3292  Trihexosylceramide (d18:1/26:1(17Z))   C0020597   \n",
       "3293       Trihexosylceramide (d18:1/24:0)   C0020597   \n",
       "3294     Tetrahexosylceramide (d18:1/12:0)   C0020597   \n",
       "3295                                5-HETE   C0035455   \n",
       "\n",
       "                              concentration sample  source_id  \n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "2     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "3     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "4                    235.0 (218.0-252.0) uM  Blood          7  \n",
       "6                    235.0 (218.0-252.0) uM  Blood          7  \n",
       "...                                     ...    ...        ...  \n",
       "3291                    0.90 (1.00-0.90) uM  Blood          7  \n",
       "3292                    0.90 (0.90-1.00) uM  Blood          7  \n",
       "3293                    0.90 (0.90-1.00) uM  Blood          7  \n",
       "3294                     1.00 (1.00-1.1) uM  Blood          7  \n",
       "3295                    0.79 (0.73-0.84) uM  Blood          7  \n",
       "\n",
       "[2763 rows x 5 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#disease_marker dataframe will contain the fields present in \"disease_maker\" table\n",
    "\n",
    "df_disease_marker = markerdb_disnet[['marker', 'disease_id', 'concentration', 'sample', 'source_id']]#\n",
    "df_disease_marker = df_disease_marker.drop_duplicates()\n",
    "df_disease_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1-Methylhistidine',\n",
       "  'C0028754',\n",
       "  '10.9 (0.80-21.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('1-Methylhistidine',\n",
       "  'C0039870',\n",
       "  '10.9 (0.80-21.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('1-Methylhistidine',\n",
       "  'C0451819',\n",
       "  '10.9 (0.80-21.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('(R)-3-Hydroxybutyric acid',\n",
       "  'C0028754',\n",
       "  '235.0 (218.0-252.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('(R)-3-Hydroxybutyric acid',\n",
       "  'C0039870',\n",
       "  '235.0 (218.0-252.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('(R)-3-Hydroxybutyric acid',\n",
       "  'C0451819',\n",
       "  '235.0 (218.0-252.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('L-Carnitine',\n",
       "  'C0028754',\n",
       "  '1.1e+04 (4.3e+03-1.8e+04) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Carnitine',\n",
       "  'C0039870',\n",
       "  '1.1e+04 (4.3e+03-1.8e+04) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Carnitine',\n",
       "  'C0451819',\n",
       "  '1.1e+04 (4.3e+03-1.8e+04) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Threonine', 'C0028754', '73.8 (44.3-103.3) uM', 'Blood', 7),\n",
       " ('L-Threonine', 'C0039870', '73.8 (44.3-103.3) uM', 'Blood', 7),\n",
       " ('L-Threonine', 'C0451819', '73.8 (44.3-103.3) uM', 'Blood', 7),\n",
       " ('Ornithine', 'C0028754', '55.2 (39.8-70.6) uM', 'Blood', 7),\n",
       " ('Ornithine', 'C0039870', '55.2 (39.8-70.6) uM', 'Blood', 7),\n",
       " ('Ornithine', 'C0451819', '55.2 (39.8-70.6) uM', 'Blood', 7),\n",
       " ('L-Hexanoylcarnitine', 'C0028754', '0.051 (0.035-0.067) uM', 'Blood', 7),\n",
       " ('L-Hexanoylcarnitine', 'C0039870', '0.051 (0.035-0.067) uM', 'Blood', 7),\n",
       " ('L-Hexanoylcarnitine', 'C0451819', '0.051 (0.035-0.067) uM', 'Blood', 7),\n",
       " ('L-Octanoylcarnitine',\n",
       "  'C0028754',\n",
       "  '69.6 (67.3-71.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Octanoylcarnitine',\n",
       "  'C0039870',\n",
       "  '69.6 (67.3-71.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Octanoylcarnitine',\n",
       "  'C0451819',\n",
       "  '69.6 (67.3-71.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Octanoylcarnitine', 'C0028754', '0.14 (0.11-0.17) uM', 'Blood', 7),\n",
       " ('L-Octanoylcarnitine', 'C0039870', '0.14 (0.11-0.17) uM', 'Blood', 7),\n",
       " ('L-Octanoylcarnitine', 'C0451819', '0.14 (0.11-0.17) uM', 'Blood', 7),\n",
       " ('Propionylcarnitine',\n",
       "  'C0028754',\n",
       "  '142.2 (128.5-155.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Propionylcarnitine',\n",
       "  'C0039870',\n",
       "  '142.2 (128.5-155.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Propionylcarnitine',\n",
       "  'C0451819',\n",
       "  '142.2 (128.5-155.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Behenic acid',\n",
       "  'C0028754',\n",
       "  '61.7 (59.4-63.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Behenic acid',\n",
       "  'C0039870',\n",
       "  '61.7 (59.4-63.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Behenic acid',\n",
       "  'C0451819',\n",
       "  '61.7 (59.4-63.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Estrone sulfate', 'C0028754', '0.0020 (0.0014-0.0030) uM', 'Blood', 7),\n",
       " ('Estrone sulfate', 'C0039870', '0.0020 (0.0014-0.0030) uM', 'Blood', 7),\n",
       " ('Estrone sulfate', 'C0451819', '0.0020 (0.0014-0.0030) uM', 'Blood', 7),\n",
       " ('Butyrylcarnitine',\n",
       "  'C0028754',\n",
       "  '565.4 (533.0-597.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Butyrylcarnitine',\n",
       "  'C0039870',\n",
       "  '565.4 (533.0-597.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Butyrylcarnitine',\n",
       "  'C0451819',\n",
       "  '565.4 (533.0-597.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Butyrylcarnitine', 'C0028754', '0.069 (0.022-0.12) uM', 'Blood', 7),\n",
       " ('Butyrylcarnitine', 'C0039870', '0.069 (0.022-0.12) uM', 'Blood', 7),\n",
       " ('Butyrylcarnitine', 'C0451819', '0.069 (0.022-0.12) uM', 'Blood', 7),\n",
       " ('cis-5-Tetradecenoylcarnitine',\n",
       "  'C0028754',\n",
       "  '0.12 (0.080-0.16) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('cis-5-Tetradecenoylcarnitine',\n",
       "  'C0039870',\n",
       "  '0.12 (0.080-0.16) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('cis-5-Tetradecenoylcarnitine',\n",
       "  'C0451819',\n",
       "  '0.12 (0.080-0.16) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('Dodecanoylcarnitine', 'C0028754', '0.080 (0.055-0.11) uM', 'Blood', 7),\n",
       " ('Dodecanoylcarnitine', 'C0039870', '0.080 (0.055-0.11) uM', 'Blood', 7),\n",
       " ('Dodecanoylcarnitine', 'C0451819', '0.080 (0.055-0.11) uM', 'Blood', 7),\n",
       " ('Zeaxanthin', 'C0028754', '0.13 (0.016-0.24) uM', 'Blood', 7),\n",
       " ('Zeaxanthin', 'C0039870', '0.13 (0.016-0.24) uM', 'Blood', 7),\n",
       " ('Zeaxanthin', 'C0451819', '0.13 (0.016-0.24) uM', 'Blood', 7),\n",
       " ('Lycopene', 'C0028754', '0.44 (0.024-0.86) uM', 'Blood', 7),\n",
       " ('Lycopene', 'C0039870', '0.44 (0.024-0.86) uM', 'Blood', 7),\n",
       " ('Lycopene', 'C0451819', '0.44 (0.024-0.86) uM', 'Blood', 7),\n",
       " ('Alpha-Carotene', 'C0028754', '0.059 (0.0090-0.11) uM', 'Blood', 7),\n",
       " ('Alpha-Carotene', 'C0039870', '0.059 (0.0090-0.11) uM', 'Blood', 7),\n",
       " ('Alpha-Carotene', 'C0451819', '0.059 (0.0090-0.11) uM', 'Blood', 7),\n",
       " ('Tetradecanoylcarnitine', 'C0028754', '0.043 (0.028-0.058) uM', 'Blood', 7),\n",
       " ('Tetradecanoylcarnitine', 'C0039870', '0.043 (0.028-0.058) uM', 'Blood', 7),\n",
       " ('Tetradecanoylcarnitine', 'C0451819', '0.043 (0.028-0.058) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(11Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(11Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(11Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:2(9Z,12Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:2(9Z,12Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:0/18:2(9Z,12Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:0/20:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:2(11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:2(11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:2(11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))', 'C0028754', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/20:4(5Z,8Z,11Z,14Z))', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/22:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/22:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(14:0/22:1(13Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/22:1(13Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/22:1(13Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:0/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:0/P-18:1(11Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/P-18:1(11Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/P-18:1(11Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/P-18:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/P-18:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:0/P-18:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '1.9 (1.8-2.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '1.9 (1.8-2.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '1.9 (1.8-2.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/18:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(11Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(11Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(11Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/18:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/20:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))', 'C0028754', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/20:3(5Z,8Z,11Z))', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/20:3(8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:4(7Z,10Z,13Z,16Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:4(7Z,10Z,13Z,16Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:4(7Z,10Z,13Z,16Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(14:1(9Z)/P-18:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(11Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(11Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(11Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(14:1(9Z)/P-18:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(16:0/16:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:0/16:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:0/16:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:0/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:2(9Z,12Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:2(9Z,12Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:2(9Z,12Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))', 'C0028754', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/18:4(6Z,9Z,12Z,15Z))', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/20:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/20:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/20:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(16:0/20:1(11Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/20:1(11Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/20:1(11Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:0/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:0/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:0/P-18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.070 (0.060-0.080) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/16:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/16:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/16:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/16:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/16:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/16:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/18:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))', 'C0028754', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/18:3(6Z,9Z,12Z))', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:2(13Z,16Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/22:2(13Z,16Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/22:2(13Z,16Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-16:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/P-16:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/P-16:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(16:1(9Z)/P-18:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(16:1(9Z)/P-18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/14:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:0/14:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:0/14:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:0/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/18:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/18:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/20:3(5Z,8Z,11Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/20:3(5Z,8Z,11Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/20:3(5Z,8Z,11Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/20:3(8Z,11Z,14Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/20:3(8Z,11Z,14Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/20:3(8Z,11Z,14Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:0/P-18:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/P-18:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/P-18:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(18:0/P-18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/P-18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/P-18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/P-18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/P-18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:0/P-18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/14:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/14:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/14:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/14:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/14:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/14:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(11Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(11Z)/P-18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/14:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/14:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/14:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/14:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/14:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/14:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/20:2(11Z,14Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/20:2(11Z,14Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/20:2(11Z,14Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:1(9Z)/P-18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/14:0)', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/14:0)', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/14:0)', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:3(9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/20:1(11Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/20:1(11Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/20:1(11Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(18:2(9Z,12Z)/20:4(8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
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       " ('PC(18:4(6Z,9Z,12Z,15Z)/16:0)', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
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       "  'C0028754',\n",
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       "  'Urine',\n",
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       " ('PC(20:0/18:3(6Z,9Z,12Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:0/18:3(6Z,9Z,12Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:0/18:3(9Z,12Z,15Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:0/18:3(9Z,12Z,15Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:0/18:3(9Z,12Z,15Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:0/P-16:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(20:0/P-16:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(20:0/P-16:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(20:1(11Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
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       " ('PC(20:1(11Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:1(11Z)/18:2(9Z,12Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:1(11Z)/18:2(9Z,12Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:1(11Z)/18:2(9Z,12Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:1(11Z)/P-16:0)',\n",
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       "  'Urine',\n",
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       "  'Urine',\n",
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       " ('PC(20:2(11Z,14Z)/14:0)',\n",
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       "  'Urine',\n",
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       " ('PC(20:2(11Z,14Z)/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
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       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
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       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
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       " ('PC(20:2(11Z,14Z)/18:1(9Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:2(11Z,14Z)/18:1(9Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:2(11Z,14Z)/18:4(6Z,9Z,12Z,15Z))',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
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       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
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       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
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       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
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       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
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       "  'C0039870',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
       "  7),\n",
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       " ('PC(20:3(5Z,8Z,11Z)/14:1(9Z))', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/14:1(9Z))', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/18:0)', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/18:0)', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/18:0)', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/18:3(6Z,9Z,12Z))',\n",
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       "  'C0039870',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       " ('PC(20:3(5Z,8Z,11Z)/P-18:0)', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(5Z,8Z,11Z)/P-18:0)', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(8Z,11Z,14Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
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       "  'C0039870',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       "  'C0028754',\n",
       "  '1.4 (0.79-2.0) uM',\n",
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       "  'C0039870',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
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       "  'C0451819',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
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       " ('PC(20:3(8Z,11Z,14Z)/18:0)', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(8Z,11Z,14Z)/18:0)', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(20:3(8Z,11Z,14Z)/18:3(6Z,9Z,12Z))',\n",
       "  'C0028754',\n",
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       "  'Urine',\n",
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       "  'C0039870',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       "  'C0028754',\n",
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       "  'Urine',\n",
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       "  'C0039870',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
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       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
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       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
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       "  'C0028754',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:4(5Z,8Z,11Z,14Z)/14:0)',\n",
       "  'C0039870',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
       "  '0.34 (0.32-0.36) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:4(5Z,8Z,11Z,14Z)/14:0)', 'C0028754', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(20:4(5Z,8Z,11Z,14Z)/14:0)', 'C0039870', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(20:4(5Z,8Z,11Z,14Z)/14:0)', 'C0451819', '1.4 (0.79-2.0) uM', 'Blood', 7),\n",
       " ('PC(20:4(5Z,8Z,11Z,14Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
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       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
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       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
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       "  'C0028754',\n",
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       "  'Urine',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       "  'Urine',\n",
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       " ('PC(20:4(8Z,11Z,14Z,17Z)/14:0)',\n",
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       "  'Urine',\n",
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       " ('PC(20:4(8Z,11Z,14Z,17Z)/14:0)',\n",
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       "  'Urine',\n",
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       " ('PC(20:4(8Z,11Z,14Z,17Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
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       "  'C0039870',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
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       "  'C0451819',\n",
       "  '1.4 (0.79-2.0) uM',\n",
       "  'Blood',\n",
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       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
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       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
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       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
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       "  'C0028754',\n",
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       "  'C0451819',\n",
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       "  'Urine',\n",
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       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
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       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
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       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
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       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
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       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
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       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
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       "  'C0028754',\n",
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       "  'Urine',\n",
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       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-16:0)',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-16:0)',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-16:0)',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:0/14:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(22:0/14:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(22:0/14:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(22:0/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:0/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:0/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:1(13Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:1(13Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:1(13Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:2(13Z,16Z)/16:1(9Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(22:2(13Z,16Z)/16:1(9Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(22:2(13Z,16Z)/16:1(9Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(22:4(7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:4(7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:4(7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:5(4Z,7Z,10Z,13Z,16Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/14:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:5(7Z,10Z,13Z,16Z,19Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0028754',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0039870',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/14:0)',\n",
       "  'C0451819',\n",
       "  '0.72 (0.44-1.0) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-16:0)',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-16:0)',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-16:0)',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/16:0)',\n",
       "  'C0028754',\n",
       "  '6.9 (6.5-7.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/16:0)',\n",
       "  'C0039870',\n",
       "  '6.9 (6.5-7.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/16:0)',\n",
       "  'C0451819',\n",
       "  '6.9 (6.5-7.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('LysoPC(18:0)',\n",
       "  'C0028754',\n",
       "  '6.1 (5.7-6.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('LysoPC(18:0)',\n",
       "  'C0039870',\n",
       "  '6.1 (5.7-6.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('LysoPC(18:0)',\n",
       "  'C0451819',\n",
       "  '6.1 (5.7-6.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/16:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/16:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/16:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:2(9Z,12Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:2(9Z,12Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/18:2(9Z,12Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/20:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/20:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/20:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-16:0/20:1(11Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/20:1(11Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/20:1(11Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/14:1(9Z))', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/14:1(9Z))', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/14:1(9Z))', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:0)', 'C0028754', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/18:0)', 'C0039870', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/18:0)', 'C0451819', '2.2 (1.4-3.0) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/18:1(11Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:1(11Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:1(11Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:1(9Z))',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:1(9Z))',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/18:1(9Z))',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:0/20:3(5Z,8Z,11Z))', 'C0028754', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/20:3(5Z,8Z,11Z))', 'C0039870', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/20:3(5Z,8Z,11Z))', 'C0451819', '56.1 (36.4-75.8) uM', 'Blood', 7),\n",
       " ('PC(P-18:0/20:3(8Z,11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:0/20:3(8Z,11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:0/20:3(8Z,11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/14:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/14:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/14:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/14:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/14:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/14:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(11Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:2(11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/14:0)', 'C0028754', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/14:0)', 'C0039870', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/14:0)', 'C0451819', '18.3 (10.7-25.9) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/14:1(9Z))', 'C0028754', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/14:1(9Z))', 'C0039870', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/14:1(9Z))', 'C0451819', '4.0 (2.4-5.6) uM', 'Blood', 7),\n",
       " ('PC(P-18:1(9Z)/16:0)',\n",
       "  'C0028754',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/16:0)',\n",
       "  'C0039870',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/16:0)',\n",
       "  'C0451819',\n",
       "  '2.9 (2.7-3.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/16:1(9Z))',\n",
       "  'C0028754',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/16:1(9Z))',\n",
       "  'C0039870',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/16:1(9Z))',\n",
       "  'C0451819',\n",
       "  '2.6 (2.3-2.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:0)',\n",
       "  'C0028754',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:0)',\n",
       "  'C0039870',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:0)',\n",
       "  'C0451819',\n",
       "  '1.8 (1.6-1.9) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0028754',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0039870',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/18:4(6Z,9Z,12Z,15Z))',\n",
       "  'C0451819',\n",
       "  '13.5 (7.4-19.6) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:2(11Z,14Z))',\n",
       "  'C0028754',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:2(11Z,14Z))',\n",
       "  'C0039870',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:2(11Z,14Z))',\n",
       "  'C0451819',\n",
       "  '56.1 (36.4-75.8) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0028754',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0039870',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(P-18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z))',\n",
       "  'C0451819',\n",
       "  '0.78 (0.70-0.86) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('4-Hydroxybenzaldehyde',\n",
       "  'C0028754',\n",
       "  '6.7 (5.4-8.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('4-Hydroxybenzaldehyde',\n",
       "  'C0039870',\n",
       "  '6.7 (5.4-8.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('4-Hydroxybenzaldehyde',\n",
       "  'C0451819',\n",
       "  '6.7 (5.4-8.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/24:1(15Z))',\n",
       "  'C0028754',\n",
       "  '3.1 (2.9-3.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/24:1(15Z))',\n",
       "  'C0039870',\n",
       "  '3.1 (2.9-3.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:1/24:1(15Z))',\n",
       "  'C0451819',\n",
       "  '3.1 (2.9-3.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxypropionylcarnitine',\n",
       "  'C0028754',\n",
       "  '2.4 (2.3-2.6) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxypropionylcarnitine',\n",
       "  'C0039870',\n",
       "  '2.4 (2.3-2.6) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxypropionylcarnitine',\n",
       "  'C0451819',\n",
       "  '2.4 (2.3-2.6) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Valerylcarnitine', 'C0028754', '0.089 (0.048-0.13) uM', 'Blood', 7),\n",
       " ('Valerylcarnitine', 'C0039870', '0.089 (0.048-0.13) uM', 'Blood', 7),\n",
       " ('Valerylcarnitine', 'C0451819', '0.089 (0.048-0.13) uM', 'Blood', 7),\n",
       " ('Hydroxyvalerylcarnitine', 'C0028754', '0.023 (0.016-0.030) uM', 'Blood', 7),\n",
       " ('Hydroxyvalerylcarnitine', 'C0039870', '0.023 (0.016-0.030) uM', 'Blood', 7),\n",
       " ('Hydroxyvalerylcarnitine', 'C0451819', '0.023 (0.016-0.030) uM', 'Blood', 7),\n",
       " ('trans-2-Dodecenoylcarnitine',\n",
       "  'C0028754',\n",
       "  '0.16 (0.11-0.21000000000000002) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('trans-2-Dodecenoylcarnitine',\n",
       "  'C0039870',\n",
       "  '0.16 (0.11-0.21000000000000002) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('trans-2-Dodecenoylcarnitine',\n",
       "  'C0451819',\n",
       "  '0.16 (0.11-0.21000000000000002) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('PC(o-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0028754',\n",
       "  '1.6 (1.6-1.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(o-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0039870',\n",
       "  '1.6 (1.6-1.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(o-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))',\n",
       "  'C0451819',\n",
       "  '1.6 (1.6-1.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('PC(o-18:1(11Z)/18:2(9Z,12Z))', 'C0028754', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('PC(o-18:1(11Z)/18:2(9Z,12Z))', 'C0039870', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('PC(o-18:1(11Z)/18:2(9Z,12Z))', 'C0451819', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('PC(o-18:1(9Z)/18:2(9Z,12Z))', 'C0028754', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('PC(o-18:1(9Z)/18:2(9Z,12Z))', 'C0039870', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('PC(o-18:1(9Z)/18:2(9Z,12Z))', 'C0451819', '8.4 (5.7-11.1) uM', 'Blood', 7),\n",
       " ('SM(d18:0/14:1(9Z)(OH))',\n",
       "  'C0028754',\n",
       "  '0.36 (0.32-0.40) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:0/14:1(9Z)(OH))',\n",
       "  'C0039870',\n",
       "  '0.36 (0.32-0.40) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:0/14:1(9Z)(OH))',\n",
       "  'C0451819',\n",
       "  '0.36 (0.32-0.40) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:0/22:1(13Z)(OH))',\n",
       "  'C0028754',\n",
       "  '2.2 (2.1-2.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:0/22:1(13Z)(OH))',\n",
       "  'C0039870',\n",
       "  '2.2 (2.1-2.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('SM(d18:0/22:1(13Z)(OH))',\n",
       "  'C0451819',\n",
       "  '2.2 (2.1-2.3) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('1-Methylhistidine',\n",
       "  'C0002395',\n",
       "  '15.7 (11.7-19.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('1-Methylhistidine',\n",
       "  'C0154671',\n",
       "  '15.7 (11.7-19.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Carnosine', 'C0002395', '18.8 (8.5-29.1) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Carnosine', 'C0154671', '18.8 (8.5-29.1) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Beta-Alanine', 'C0002395', '2.8 (1.4-4.3) uM', 'Saliva', 7),\n",
       " ('Beta-Alanine', 'C0154671', '2.8 (1.4-4.3) uM', 'Saliva', 7),\n",
       " ('cis-Aconitic acid', 'C0002395', '2.8 (0.69-4.9) uM', 'Saliva', 7),\n",
       " ('cis-Aconitic acid', 'C0154671', '2.8 (0.69-4.9) uM', 'Saliva', 7),\n",
       " ('Dopamine', 'C0002395', '18.0 (15.6-20.5) uM', 'Blood', 7),\n",
       " ('Dopamine', 'C0154671', '18.0 (15.6-20.5) uM', 'Blood', 7),\n",
       " ('Glycerophosphocholine',\n",
       "  'C0002395',\n",
       "  '6.9 (3.7-10.1) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Glycerophosphocholine',\n",
       "  'C0154671',\n",
       "  '6.9 (3.7-10.1) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Cytidine', 'C0002395', '0.39 (0.090-0.69) uM', 'Saliva', 7),\n",
       " ('Cytidine', 'C0154671', '0.39 (0.090-0.69) uM', 'Saliva', 7),\n",
       " ('Choline', 'C0002395', '2.5 (1.5-3.5) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Choline', 'C0154671', '2.5 (1.5-3.5) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Cystathionine',\n",
       "  'C0002395',\n",
       "  '1.7 (1.2-2.2) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Cystathionine',\n",
       "  'C0154671',\n",
       "  '1.7 (1.2-2.2) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Cystathionine', 'C0002395', '1.0 (0.81-1.3) uM', 'Blood', 7),\n",
       " ('L-Cystathionine', 'C0154671', '1.0 (0.81-1.3) uM', 'Blood', 7),\n",
       " ('Gamma-Aminobutyric acid', 'C0002395', '1.8 (1.6-2.1) uM', 'Blood', 7),\n",
       " ('Gamma-Aminobutyric acid', 'C0154671', '1.8 (1.6-2.1) uM', 'Blood', 7),\n",
       " ('Glycine',\n",
       "  'C0002395',\n",
       "  '0.0100 (0.0090-0.011) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Glycine',\n",
       "  'C0154671',\n",
       "  '0.0100 (0.0090-0.011) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Glycine', 'C0002395', '164.2 (109.6-218.9) uM', 'Saliva', 7),\n",
       " ('Glycine', 'C0154671', '164.2 (109.6-218.9) uM', 'Saliva', 7),\n",
       " ('Glycine', 'C0002395', '0.28 (0.16-0.40) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Glycine', 'C0154671', '0.28 (0.16-0.40) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Glycine', 'C0002395', '0.15 (0.14-0.17) uM', 'Blood', 7),\n",
       " ('Glycine', 'C0154671', '0.15 (0.14-0.17) uM', 'Blood', 7),\n",
       " ('Glycerol 3-phosphate', 'C0002395', '14.8 (3.4-26.2) uM', 'Saliva', 7),\n",
       " ('Glycerol 3-phosphate', 'C0154671', '14.8 (3.4-26.2) uM', 'Saliva', 7),\n",
       " ('Guanine', 'C0002395', '1.4 (1.2-1.5) uM', 'Saliva', 7),\n",
       " ('Guanine', 'C0154671', '1.4 (1.2-1.5) uM', 'Saliva', 7),\n",
       " ('Fumaric acid', 'C0002395', '11.0 (2.0-20.0) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Fumaric acid', 'C0154671', '11.0 (2.0-20.0) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Malic acid', 'C0002395', '22.3 (7.6-36.9) uM', 'Saliva', 7),\n",
       " ('L-Malic acid', 'C0154671', '22.3 (7.6-36.9) uM', 'Saliva', 7),\n",
       " ('Hypoxanthine', 'C0002395', '2.6 (1.5-3.7) uM', 'Saliva', 7),\n",
       " ('Hypoxanthine', 'C0154671', '2.6 (1.5-3.7) uM', 'Saliva', 7),\n",
       " ('L-Tyrosine', 'C0002395', '27.2 (22.4-32.0) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Tyrosine', 'C0154671', '27.2 (22.4-32.0) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Tyrosine', 'C0002395', '143.8 (128.3-159.3) uM', 'Blood', 7),\n",
       " ('L-Tyrosine', 'C0154671', '143.8 (128.3-159.3) uM', 'Blood', 7),\n",
       " ('L-Phenylalanine',\n",
       "  'C0002395',\n",
       "  '1.4 (1.2-1.6) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Phenylalanine',\n",
       "  'C0154671',\n",
       "  '1.4 (1.2-1.6) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Phenylalanine', 'C0002395', '6.0 (5.9-6.1) uM', 'Saliva', 7),\n",
       " ('L-Phenylalanine', 'C0154671', '6.0 (5.9-6.1) uM', 'Saliva', 7),\n",
       " ('L-Alanine', 'C0002395', '40.5 (26.7-54.3) uM', 'Saliva', 7),\n",
       " ('L-Alanine', 'C0154671', '40.5 (26.7-54.3) uM', 'Saliva', 7),\n",
       " ('L-Threonine',\n",
       "  'C0002395',\n",
       "  '0.030 (0.027-0.033) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Threonine',\n",
       "  'C0154671',\n",
       "  '0.030 (0.027-0.033) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Isoleucine',\n",
       "  'C0002395',\n",
       "  '0.13 (0.090-0.17) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Isoleucine',\n",
       "  'C0154671',\n",
       "  '0.13 (0.090-0.17) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Isoleucine', 'C0002395', '1.5e+03 (1.1e+03-1.9e+03) uM', 'Blood', 7),\n",
       " ('L-Isoleucine', 'C0154671', '1.5e+03 (1.1e+03-1.9e+03) uM', 'Blood', 7),\n",
       " ('L-Histidine',\n",
       "  'C0002395',\n",
       "  '30.5 (26.2-34.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Histidine',\n",
       "  'C0154671',\n",
       "  '30.5 (26.2-34.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Histidine', 'C0002395', '17.0 (9.5-24.4) uM', 'Saliva', 7),\n",
       " ('L-Histidine', 'C0154671', '17.0 (9.5-24.4) uM', 'Saliva', 7),\n",
       " ('L-Histidine', 'C0002395', '116.7 (98.5-134.8) uM', 'Blood', 7),\n",
       " ('L-Histidine', 'C0154671', '116.7 (98.5-134.8) uM', 'Blood', 7),\n",
       " ('L-Dopa', 'C0002395', '14.0 (11.5-16.5) uM', 'Blood', 7),\n",
       " ('L-Dopa', 'C0154671', '14.0 (11.5-16.5) uM', 'Blood', 7),\n",
       " ('L-Lysine', 'C0002395', '5.5 (3.8-7.2) umol/mmol creatinine', 'Urine', 7),\n",
       " ('L-Lysine', 'C0154671', '5.5 (3.8-7.2) umol/mmol creatinine', 'Urine', 7),\n",
       " ('L-Lysine', 'C0002395', '44.6 (33.8-55.4) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Lysine', 'C0154671', '44.6 (33.8-55.4) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Lysine', 'C0002395', '216.7 (162.2-271.2) uM', 'Blood', 7),\n",
       " ('L-Lysine', 'C0154671', '216.7 (162.2-271.2) uM', 'Blood', 7),\n",
       " ('L-Serine', 'C0002395', '26.0 (20.6-31.4) uM', 'Saliva', 7),\n",
       " ('L-Serine', 'C0154671', '26.0 (20.6-31.4) uM', 'Saliva', 7),\n",
       " ('L-Serine', 'C0002395', '0.21 (0.19-0.23) uM', 'Blood', 7),\n",
       " ('L-Serine', 'C0154671', '0.21 (0.19-0.23) uM', 'Blood', 7),\n",
       " ('L-Lactic acid',\n",
       "  'C0002395',\n",
       "  '4.6e+03 (1.7e+03-7.5e+03) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('L-Lactic acid',\n",
       "  'C0154671',\n",
       "  '4.6e+03 (1.7e+03-7.5e+03) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('L-Cystine',\n",
       "  'C0002395',\n",
       "  '72.0 (60.0-84.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Cystine',\n",
       "  'C0154671',\n",
       "  '72.0 (60.0-84.0) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Cystine', 'C0002395', '209.0 (163.0-255.0) uM', 'Blood', 7),\n",
       " ('L-Cystine', 'C0154671', '209.0 (163.0-255.0) uM', 'Blood', 7),\n",
       " ('Isocitric acid', 'C0002395', '3.8 (0.86-6.6) uM', 'Saliva', 7),\n",
       " ('Isocitric acid', 'C0154671', '3.8 (0.86-6.6) uM', 'Saliva', 7),\n",
       " ('Anserine', 'C0002395', '8.0 (3.0-13.0) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Anserine', 'C0154671', '8.0 (3.0-13.0) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Anserine', 'C0002395', '0.46 (0.24-0.67) uM', 'Blood', 7),\n",
       " ('Anserine', 'C0154671', '0.46 (0.24-0.67) uM', 'Blood', 7),\n",
       " ('Ornithine', 'C0002395', '12.3 (7.3-17.4) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Ornithine', 'C0154671', '12.3 (7.3-17.4) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Ornithine', 'C0002395', '28.3 (18.5-38.0) uM', 'Saliva', 7),\n",
       " ('Ornithine', 'C0154671', '28.3 (18.5-38.0) uM', 'Saliva', 7),\n",
       " ('Succinic acid',\n",
       "  'C0002395',\n",
       "  '55.0 (19.0-91.0) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Succinic acid',\n",
       "  'C0154671',\n",
       "  '55.0 (19.0-91.0) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " (\"Uridine 5'-monophosphate\", 'C0002395', '1.2 (0.65-1.8) uM', 'Saliva', 7),\n",
       " (\"Uridine 5'-monophosphate\", 'C0154671', '1.2 (0.65-1.8) uM', 'Saliva', 7),\n",
       " ('5-Hydroxylysine', 'C0002395', '0.50 (0.47-0.53) uM', 'Blood', 7),\n",
       " ('5-Hydroxylysine', 'C0154671', '0.50 (0.47-0.53) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C0002395', '5.2 (3.9-6.4) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C0154671', '5.2 (3.9-6.4) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C0002395', '6.0 (4.6-7.4) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Arginine', 'C0154671', '6.0 (4.6-7.4) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Arginine', 'C0002395', '91.3 (78.2-104.4) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C0154671', '91.3 (78.2-104.4) uM', 'Blood', 7),\n",
       " ('D-Ribulose 5-phosphate', 'C0002395', '3.4 (1.6-5.2) uM', 'Saliva', 7),\n",
       " ('D-Ribulose 5-phosphate', 'C0154671', '3.4 (1.6-5.2) uM', 'Saliva', 7),\n",
       " ('L-Glutamine',\n",
       "  'C0002395',\n",
       "  '1.2 (0.68-1.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Glutamine',\n",
       "  'C0154671',\n",
       "  '1.2 (0.68-1.7) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Glutamine', 'C0002395', '228.3 (216.3-240.3) uM', 'Blood', 7),\n",
       " ('L-Glutamine', 'C0154671', '228.3 (216.3-240.3) uM', 'Blood', 7),\n",
       " ('L-Leucine', 'C0002395', '0.91 (0.80-1.0) umol/mmol creatinine', 'Urine', 7),\n",
       " ('L-Leucine', 'C0154671', '0.91 (0.80-1.0) umol/mmol creatinine', 'Urine', 7),\n",
       " ('Iron', 'C0002395', '2.5 (0.70-4.3) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Iron', 'C0154671', '2.5 (0.70-4.3) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Glycylproline',\n",
       "  'C0002395',\n",
       "  '0.0087 (0.0071-0.010) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Glycylproline',\n",
       "  'C0154671',\n",
       "  '0.0087 (0.0071-0.010) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxyproline',\n",
       "  'C0002395',\n",
       "  '0.21 (0.19-0.23) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxyproline',\n",
       "  'C0154671',\n",
       "  '0.21 (0.19-0.23) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Hydroxyproline', 'C0002395', '5.6 (4.9-6.3) uM', 'Blood', 7),\n",
       " ('Hydroxyproline', 'C0154671', '5.6 (4.9-6.3) uM', 'Blood', 7),\n",
       " ('Homocysteine',\n",
       "  'C0002395',\n",
       "  '0.22 (0.094-0.34) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Homocysteine',\n",
       "  'C0154671',\n",
       "  '0.22 (0.094-0.34) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('L-Valine', 'C0002395', '0.73 (0.61-0.85) umol/mmol creatinine', 'Urine', 7),\n",
       " ('L-Valine', 'C0154671', '0.73 (0.61-0.85) umol/mmol creatinine', 'Urine', 7),\n",
       " ('L-Valine', 'C0002395', '4.1 (2.9-5.3) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Valine', 'C0154671', '4.1 (2.9-5.3) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('L-Valine', 'C0002395', '58.3 (43.5-73.0) uM', 'Blood', 7),\n",
       " ('L-Valine', 'C0154671', '58.3 (43.5-73.0) uM', 'Blood', 7),\n",
       " ('Acetylcholine',\n",
       "  'C0002395',\n",
       "  '0.0100 (0.0050-0.015) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Acetylcholine',\n",
       "  'C0154671',\n",
       "  '0.0100 (0.0050-0.015) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Citrulline',\n",
       "  'C0002395',\n",
       "  '21.0 (18.2-23.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Citrulline',\n",
       "  'C0154671',\n",
       "  '21.0 (18.2-23.8) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('Citrulline', 'C0002395', '27.0 (24.4-29.6) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Citrulline', 'C0154671', '27.0 (24.4-29.6) uM', 'Cerebrospinal_Fluid', 7),\n",
       " ('Citrulline', 'C0002395', '95.0 (83.8-106.2) uM', 'Blood', 7),\n",
       " ('Citrulline', 'C0154671', '95.0 (83.8-106.2) uM', 'Blood', 7),\n",
       " ('L-Tryptophan',\n",
       "  'C0002395',\n",
       "  '1.2 (0.96-1.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('L-Tryptophan',\n",
       "  'C0154671',\n",
       "  '1.2 (0.96-1.4) umol/mmol creatinine',\n",
       "  'Urine',\n",
       "  7),\n",
       " ('S-Adenosylhomocysteine',\n",
       "  'C0002395',\n",
       "  '0.027 (0.021-0.033) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('S-Adenosylhomocysteine',\n",
       "  'C0154671',\n",
       "  '0.027 (0.021-0.033) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('DHEA sulfate', 'C0002395', '6.5 (4.0-9.1) uM', 'Blood', 7),\n",
       " ('DHEA sulfate', 'C0154671', '6.5 (4.0-9.1) uM', 'Blood', 7),\n",
       " ('Manganese', 'C0002395', '0.011 (0.011-0.011) uM', 'Blood', 7),\n",
       " ('Manganese', 'C0154671', '0.011 (0.011-0.011) uM', 'Blood', 7),\n",
       " ('24-Hydroxycholesterol',\n",
       "  'C0002395',\n",
       "  '0.0061 (0.0058-0.0064) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('24-Hydroxycholesterol',\n",
       "  'C0154671',\n",
       "  '0.0061 (0.0058-0.0064) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Phosphorylcholine',\n",
       "  'C0002395',\n",
       "  '2.2 (1.3-3.0) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Phosphorylcholine',\n",
       "  'C0154671',\n",
       "  '2.2 (1.3-3.0) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Substance P',\n",
       "  'C0002395',\n",
       "  '2.8e-05 (2.6e-05-3.0e-05) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Substance P',\n",
       "  'C0154671',\n",
       "  '2.8e-05 (2.6e-05-3.0e-05) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Tungsten', 'C0002395', '0.00016 (5.0e-05-0.00027) uM', 'Blood', 7),\n",
       " ('Tungsten', 'C0154671', '0.00016 (5.0e-05-0.00027) uM', 'Blood', 7),\n",
       " ('8-Hydroxyguanine',\n",
       "  'C0002395',\n",
       "  '0.19 (0.15-0.23) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('8-Hydroxyguanine',\n",
       "  'C0154671',\n",
       "  '0.19 (0.15-0.23) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('8-Hydroxyguanosine',\n",
       "  'C0002395',\n",
       "  '0.00050 (0.00029-0.00071) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('8-Hydroxyguanosine',\n",
       "  'C0154671',\n",
       "  '0.00050 (0.00029-0.00071) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('27-Hydroxycholesterol',\n",
       "  'C0002395',\n",
       "  '0.0038 (0.0035-0.0041) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('27-Hydroxycholesterol',\n",
       "  'C0154671',\n",
       "  '0.0038 (0.0035-0.0041) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Oxidized glutathione', 'C0002395', '0.39 (0.21-0.57) uM', 'Saliva', 7),\n",
       " ('Oxidized glutathione', 'C0154671', '0.39 (0.21-0.57) uM', 'Saliva', 7),\n",
       " ('Mercury', 'C0002395', '0.0088 (0.0049-0.013) uM', 'Blood', 7),\n",
       " ('Mercury', 'C0154671', '0.0088 (0.0049-0.013) uM', 'Blood', 7),\n",
       " ('Lead', 'C0002395', '0.0021 (0.00080-0.0034) uM', 'Blood', 7),\n",
       " ('Lead', 'C0154671', '0.0021 (0.00080-0.0034) uM', 'Blood', 7),\n",
       " ('Prolylhydroxyproline',\n",
       "  'C0002395',\n",
       "  '0.086 (0.070-0.10) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('Prolylhydroxyproline',\n",
       "  'C0154671',\n",
       "  '0.086 (0.070-0.10) uM',\n",
       "  'Cerebrospinal_Fluid',\n",
       "  7),\n",
       " ('1-Methylhistidine', 'C0022661', '28.8 (10.6-47.0) uM', 'Blood', 7),\n",
       " ('1-Methylhistidine', 'C0403447', '28.8 (10.6-47.0) uM', 'Blood', 7),\n",
       " ('1-Methylhistidine', 'C1561643', '28.8 (10.6-47.0) uM', 'Blood', 7),\n",
       " ('1-Methylhistidine', 'C1579029', '28.8 (10.6-47.0) uM', 'Blood', 7),\n",
       " ('Dihydrobiopterin', 'C0022661', '0.064 (0.059-0.068) uM', 'Blood', 7),\n",
       " ('Dihydrobiopterin', 'C0403447', '0.064 (0.059-0.068) uM', 'Blood', 7),\n",
       " ('Dihydrobiopterin', 'C1561643', '0.064 (0.059-0.068) uM', 'Blood', 7),\n",
       " ('Dihydrobiopterin', 'C1579029', '0.064 (0.059-0.068) uM', 'Blood', 7),\n",
       " ('Betaine', 'C0022661', '32.5 (23.9-42.5) uM', 'Blood', 7),\n",
       " ('Betaine', 'C0403447', '32.5 (23.9-42.5) uM', 'Blood', 7),\n",
       " ('Betaine', 'C1561643', '32.5 (23.9-42.5) uM', 'Blood', 7),\n",
       " ('Betaine', 'C1579029', '32.5 (23.9-42.5) uM', 'Blood', 7),\n",
       " ('Dimethylglycine', 'C0022661', '4.8 (3.6-6.2) uM', 'Blood', 7),\n",
       " ('Dimethylglycine', 'C0403447', '4.8 (3.6-6.2) uM', 'Blood', 7),\n",
       " ('Dimethylglycine', 'C1561643', '4.8 (3.6-6.2) uM', 'Blood', 7),\n",
       " ('Dimethylglycine', 'C1579029', '4.8 (3.6-6.2) uM', 'Blood', 7),\n",
       " ('D-Xylose', 'C0022661', '2.9e+03 (2.0e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('D-Xylose', 'C0403447', '2.9e+03 (2.0e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('D-Xylose', 'C1561643', '2.9e+03 (2.0e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('D-Xylose', 'C1579029', '2.9e+03 (2.0e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('Ethanol', 'C0022661', '90.0 (10.0-170.0) uM', 'Blood', 7),\n",
       " ('Ethanol', 'C0403447', '90.0 (10.0-170.0) uM', 'Blood', 7),\n",
       " ('Ethanol', 'C1561643', '90.0 (10.0-170.0) uM', 'Blood', 7),\n",
       " ('Ethanol', 'C1579029', '90.0 (10.0-170.0) uM', 'Blood', 7),\n",
       " ('Guanidoacetic acid', 'C0022661', '2.4 (1.7-3.1) uM', 'Blood', 7),\n",
       " ('Guanidoacetic acid', 'C0403447', '2.4 (1.7-3.1) uM', 'Blood', 7),\n",
       " ('Guanidoacetic acid', 'C1561643', '2.4 (1.7-3.1) uM', 'Blood', 7),\n",
       " ('Guanidoacetic acid', 'C1579029', '2.4 (1.7-3.1) uM', 'Blood', 7),\n",
       " ('Phenylacetic acid', 'C0022661', '3.5e+03 (3.2e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('Phenylacetic acid', 'C0403447', '3.5e+03 (3.2e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('Phenylacetic acid', 'C1561643', '3.5e+03 (3.2e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('Phenylacetic acid', 'C1579029', '3.5e+03 (3.2e+03-3.8e+03) uM', 'Blood', 7),\n",
       " ('Allantoin', 'C0022661', '20.5 (14.0-27.0) uM', 'Blood', 7),\n",
       " ('Allantoin', 'C0403447', '20.5 (14.0-27.0) uM', 'Blood', 7),\n",
       " ('Allantoin', 'C1561643', '20.5 (14.0-27.0) uM', 'Blood', 7),\n",
       " ('Allantoin', 'C1579029', '20.5 (14.0-27.0) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C0022661', '13.0 (2.6-23.4) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C0403447', '13.0 (2.6-23.4) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C1561643', '13.0 (2.6-23.4) uM', 'Blood', 7),\n",
       " ('3-Methylhistidine', 'C1579029', '13.0 (2.6-23.4) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C0022661', '101.6 (62.4-140.8) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C0403447', '101.6 (62.4-140.8) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C1561643', '101.6 (62.4-140.8) uM', 'Blood', 7),\n",
       " ('L-Arginine', 'C1579029', '101.6 (62.4-140.8) uM', 'Blood', 7),\n",
       " ('Creatinine', 'C0022661', '440.7 (396.5-484.9) uM', 'Blood', 7),\n",
       " ('Creatinine', 'C0403447', '440.7 (396.5-484.9) uM', 'Blood', 7),\n",
       " ('Creatinine', 'C1561643', '440.7 (396.5-484.9) uM', 'Blood', 7),\n",
       " ('Creatinine', 'C1579029', '440.7 (396.5-484.9) uM', 'Blood', 7),\n",
       " ('Homocysteine', 'C0022661', '21.0 (18.5-25.7) uM', 'Blood', 7),\n",
       " ('Homocysteine', 'C0403447', '21.0 (18.5-25.7) uM', 'Blood', 7),\n",
       " ('Homocysteine', 'C1561643', '21.0 (18.5-25.7) uM', 'Blood', 7),\n",
       " ('Homocysteine', 'C1579029', '21.0 (18.5-25.7) uM', 'Blood', 7),\n",
       " ('Histamine', 'C0022661', '0.0024 (0.0015-0.0032) uM', 'Blood', 7),\n",
       " ('Histamine', 'C0403447', '0.0024 (0.0015-0.0032) uM', 'Blood', 7),\n",
       " ('Histamine', 'C1561643', '0.0024 (0.0015-0.0032) uM', 'Blood', 7),\n",
       " ('Histamine', 'C1579029', '0.0024 (0.0015-0.0032) uM', 'Blood', 7),\n",
       " ('Trimethylamine', 'C0022661', '1.5 (1.00-2.0) uM', 'Blood', 7),\n",
       " ('Trimethylamine', 'C0403447', '1.5 (1.00-2.0) uM', 'Blood', 7),\n",
       " ('Trimethylamine', 'C1561643', '1.5 (1.00-2.0) uM', 'Blood', 7),\n",
       " ('Trimethylamine', 'C1579029', '1.5 (1.00-2.0) uM', 'Blood', 7),\n",
       " ('Trimethylamine N-oxide', 'C0022661', '99.9 (68.0-131.8) uM', 'Blood', 7),\n",
       " ('Trimethylamine N-oxide', 'C0403447', '99.9 (68.0-131.8) uM', 'Blood', 7),\n",
       " ('Trimethylamine N-oxide', 'C1561643', '99.9 (68.0-131.8) uM', 'Blood', 7),\n",
       " ('Trimethylamine N-oxide', 'C1579029', '99.9 (68.0-131.8) uM', 'Blood', 7),\n",
       " ('Methylguanidine', 'C0022661', '3.3 (2.0-4.6) uM', 'Blood', 7),\n",
       " ('Methylguanidine', 'C0403447', '3.3 (2.0-4.6) uM', 'Blood', 7),\n",
       " ('Methylguanidine', 'C1561643', '3.3 (2.0-4.6) uM', 'Blood', 7),\n",
       " ('Methylguanidine', 'C1579029', '3.3 (2.0-4.6) uM', 'Blood', 7),\n",
       " ('Asymmetric Dimethyl-L-arginine',\n",
       "  'C0022661',\n",
       "  '0.91 (0.87-0.95) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('Asymmetric Dimethyl-L-arginine',\n",
       "  'C0403447',\n",
       "  '0.91 (0.87-0.95) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('Asymmetric Dimethyl-L-arginine',\n",
       "  'C1561643',\n",
       "  '0.91 (0.87-0.95) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('Asymmetric Dimethyl-L-arginine',\n",
       "  'C1579029',\n",
       "  '0.91 (0.87-0.95) uM',\n",
       "  'Blood',\n",
       "  7),\n",
       " ('1-Methylguanosine', 'C0022661', '0.099 (0.078-0.12) uM', 'Blood', 7),\n",
       " ('1-Methylguanosine', 'C0403447', '0.099 (0.078-0.12) uM', 'Blood', 7),\n",
       " ('1-Methylguanosine', 'C1561643', '0.099 (0.078-0.12) uM', 'Blood', 7),\n",
       " ('1-Methylguanosine', 'C1579029', '0.099 (0.078-0.12) uM', 'Blood', 7),\n",
       " ('Guanidine', 'C0022661', '3.1 (2.0-4.2) uM', 'Blood', 7),\n",
       " ('Guanidine', 'C0403447', '3.1 (2.0-4.2) uM', 'Blood', 7),\n",
       " ('Guanidine', 'C1561643', '3.1 (2.0-4.2) uM', 'Blood', 7),\n",
       " ('Guanidine', 'C1579029', '3.1 (2.0-4.2) uM', 'Blood', 7),\n",
       " ...]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As one of the parameters the function used to incoroporate the information from markerdb of diseases \n",
    "#associated to chemical diagnostic markers has is a tuple, a list of tuples from each row from \n",
    "#the dataframe 'df_disease_marker', will be generated in this cell. \n",
    "\n",
    "tuples_disease_marker = [tuple(x) for x in df_disease_marker.values]\n",
    "tuples_disease_marker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Function to insert into DISNET data related to chemical diagnostic markers associated to diseases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def disease_marker_insertion(result,cnx, logfile):\n",
    "    \n",
    "    \"\"\"Method that inserts to DISNET MySQL DB, via mysql.connector objects a list of tuples corresponding to different chemical makers associated\n",
    "    with diseases and other features'\n",
    "    \"\"\"\n",
    "\n",
    "    insert_disease_marker_classtable = \"\"\"\n",
    "            INSERT INTO disease_marker (marker, disease_id, concentration, sample, source_id) \n",
    "            VALUES (%s, %s, %s, %s, %s);\n",
    "            \"\"\"\n",
    "    cursor = cnx.cursor(buffered = True)\n",
    "    \n",
    "    try:\n",
    "        cursor.execute('''SELECT * from disease_marker''')\n",
    "#         for (a, b, c) in cursor:\n",
    "#             print(a,\" \",b,\" \",c)\n",
    "        num_fields = len(cursor.description)\n",
    "        field_names = [i[0] for i in cursor.description]\n",
    "        print(field_names)\n",
    "        '''\n",
    "        for i in result:\n",
    "            print(i)\n",
    "            cursor.execute(insert_rnaclasstable,i)\n",
    "            #print(i)\n",
    "        '''\n",
    "        cursor.executemany(insert_disease_marker_classtable, result)\n",
    "        cnx.commit()\n",
    "    \n",
    "    except mysql.connector.Error as err:\n",
    "        logfile.write('Module: ' + str(__name__) + \"\\n\" + str(err.msg))\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Function \"disease_marker_insertion\" call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#disease_marker_insertion(tuples_disease_marker,cnx, 'fichero.txt') #uncomment this line to execute\n",
    "#disease_marker_insertion function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive analysis of the inserted data into DISNET database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source_id\n",
       "count     2763.0\n",
       "mean         7.0\n",
       "std          0.0\n",
       "min          7.0\n",
       "25%          7.0\n",
       "50%          7.0\n",
       "75%          7.0\n",
       "max          7.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disease_marker.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "disease_id    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_marker_diseases_counts = df_disease_marker[['disease_id']].count()\n",
    "df_marker_diseases_counts.groupby(['disease_id']).count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode disease is: 0    C0028754\n",
      "1    C0039870\n",
      "2    C0451819\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>341.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.102639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.981959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease_id\n",
       "count  341.000000\n",
       "mean     8.102639\n",
       "std     24.981959\n",
       "min      1.000000\n",
       "25%      1.000000\n",
       "50%      2.000000\n",
       "75%      7.000000\n",
       "max    252.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_marker_diseases = df_disease_marker['disease_id'].value_counts().to_frame()\n",
    "print(\"mode disease is: \" + str(df_disease_marker['disease_id'].mode()))\n",
    "df_marker_diseases.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode biomaker is: 0    L-Phenylalanine\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>618.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.470874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.225410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           marker\n",
       "count  618.000000\n",
       "mean     4.470874\n",
       "std      4.225410\n",
       "min      1.000000\n",
       "25%      2.000000\n",
       "50%      3.000000\n",
       "75%      5.000000\n",
       "max     31.000000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_only = df_disease_marker['marker'].value_counts().to_frame()\n",
    "print(\"mode biomaker is: \" + str(df_disease_marker['marker'].mode()))\n",
    "marker_only.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L-Phenylalanine                    31\n",
      "L-Arginine                         30\n",
      "L-Valine                           27\n",
      "Pipecolic acid                     24\n",
      "Glycine                            23\n",
      "                                   ..\n",
      "Gamma-Linolenic acid                1\n",
      "Stearic acid                        1\n",
      "Glycerol                            1\n",
      "Thymine                             1\n",
      "Trihexosylceramide (d18:1/18:0)     1\n",
      "Name: marker, Length: 618, dtype: int64\n",
      "-----------------------------------------\n",
      "C0039870    252\n",
      "C0028754    252\n",
      "C0451819    252\n",
      "C0002395     82\n",
      "C0154671     82\n",
      "           ... \n",
      "C0699791      1\n",
      "C0238052      1\n",
      "C1708349      1\n",
      "C0014038      1\n",
      "C0220993      1\n",
      "Name: disease_id, Length: 341, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_disease_marker['marker'].value_counts())\n",
    "print(\"-----------------------------------------\")\n",
    "print(df_disease_marker['disease_id'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obtaining the number of times a biomarker appears in a biomarker-disease-biomarker concentration-sample association**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       biomarker appereances\n",
      "count             618.000000\n",
      "mean                4.470874\n",
      "std                 4.225410\n",
      "min                 1.000000\n",
      "25%                 2.000000\n",
      "50%                 3.000000\n",
      "75%                 5.000000\n",
      "max                31.000000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>biomarker appereances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arginine</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Valine</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pipecolic acid</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycine</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gamma-Linolenic acid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stearic acid</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerol</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trihexosylceramide (d18:1/18:0)</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>618 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 biomarker appereances\n",
       "L-Phenylalanine                                     31\n",
       "L-Arginine                                          30\n",
       "L-Valine                                            27\n",
       "Pipecolic acid                                      24\n",
       "Glycine                                             23\n",
       "...                                                ...\n",
       "Gamma-Linolenic acid                                 1\n",
       "Stearic acid                                         1\n",
       "Glycerol                                             1\n",
       "Thymine                                              1\n",
       "Trihexosylceramide (d18:1/18:0)                      1\n",
       "\n",
       "[618 rows x 1 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomarker_counts = df_disease_marker['marker'].value_counts().to_frame()\n",
    "biomarker_counts = biomarker_counts.rename(columns={'marker': 'biomarker appereances'})#biomarker appereances in an association\n",
    "print(biomarker_counts.describe())\n",
    "biomarker_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of the number of times a biomarker is in an association')]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "import seaborn as sns\n",
    "\n",
    "ax = sns.boxplot( x=biomarker_counts['biomarker appereances'] )\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set(xlabel='log (number of disease appereances in associations)', ylabel='biomarkers')\n",
    "ax.set(title='Distribution of the number of times a biomarker is in an association')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obtaining the number of times a disease appears in a biomarker-disease-biomarker concentration-sample association**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       disease appereances\n",
      "count           341.000000\n",
      "mean              8.102639\n",
      "std              24.981959\n",
      "min               1.000000\n",
      "25%               1.000000\n",
      "50%               2.000000\n",
      "75%               7.000000\n",
      "max             252.000000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease appereances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C0039870</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0028754</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0451819</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0002395</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0154671</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0699791</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0238052</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1708349</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0014038</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0220993</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          disease appereances\n",
       "C0039870                  252\n",
       "C0028754                  252\n",
       "C0451819                  252\n",
       "C0002395                   82\n",
       "C0154671                   82\n",
       "...                       ...\n",
       "C0699791                    1\n",
       "C0238052                    1\n",
       "C1708349                    1\n",
       "C0014038                    1\n",
       "C0220993                    1\n",
       "\n",
       "[341 rows x 1 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_counts = df_disease_marker['disease_id'].value_counts().to_frame()\n",
    "disease_counts = disease_counts.rename(columns={'disease_id': 'disease appereances'})\n",
    "print(disease_counts.describe())\n",
    "disease_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of the number of times a disease is in an association')]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#associations refers to biomarker-disease-biomarker concentration-sample\n",
    "ax = sns.boxplot( x=disease_counts['disease appereances'] )\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set(xlabel='log (number of disease appereances in associations)', ylabel='diseases')\n",
    "ax.set(title='Distribution of the number of times a disease is in an association')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0039870</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0451819</td>\n",
       "      <td>10.9 (0.80-21.0) umol/mmol creatinine</td>\n",
       "      <td>Urine</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>235.0 (218.0-252.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>C0039870</td>\n",
       "      <td>235.0 (218.0-252.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3291</th>\n",
       "      <td>Trihexosylceramide (d18:1/22:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (1.00-0.90) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>Trihexosylceramide (d18:1/26:1(17Z))</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>Trihexosylceramide (d18:1/24:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>0.90 (0.90-1.00) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3294</th>\n",
       "      <td>Tetrahexosylceramide (d18:1/12:0)</td>\n",
       "      <td>C0020597</td>\n",
       "      <td>1.00 (1.00-1.1) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>5-HETE</td>\n",
       "      <td>C0035455</td>\n",
       "      <td>0.79 (0.73-0.84) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2763 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    marker disease_id  \\\n",
       "0                        1-Methylhistidine   C0028754   \n",
       "2                        1-Methylhistidine   C0039870   \n",
       "3                        1-Methylhistidine   C0451819   \n",
       "4                (R)-3-Hydroxybutyric acid   C0028754   \n",
       "6                (R)-3-Hydroxybutyric acid   C0039870   \n",
       "...                                    ...        ...   \n",
       "3291       Trihexosylceramide (d18:1/22:0)   C0020597   \n",
       "3292  Trihexosylceramide (d18:1/26:1(17Z))   C0020597   \n",
       "3293       Trihexosylceramide (d18:1/24:0)   C0020597   \n",
       "3294     Tetrahexosylceramide (d18:1/12:0)   C0020597   \n",
       "3295                                5-HETE   C0035455   \n",
       "\n",
       "                              concentration sample  source_id  \n",
       "0     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "2     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "3     10.9 (0.80-21.0) umol/mmol creatinine  Urine          7  \n",
       "4                    235.0 (218.0-252.0) uM  Blood          7  \n",
       "6                    235.0 (218.0-252.0) uM  Blood          7  \n",
       "...                                     ...    ...        ...  \n",
       "3291                    0.90 (1.00-0.90) uM  Blood          7  \n",
       "3292                    0.90 (0.90-1.00) uM  Blood          7  \n",
       "3293                    0.90 (0.90-1.00) uM  Blood          7  \n",
       "3294                     1.00 (1.00-1.1) uM  Blood          7  \n",
       "3295                    0.79 (0.73-0.84) uM  Blood          7  \n",
       "\n",
       "[2763 rows x 5 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disease_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>concentration</th>\n",
       "      <th>sample</th>\n",
       "      <th>source_id</th>\n",
       "      <th>source_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>vocab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L-Valine</td>\n",
       "      <td>obesity</td>\n",
       "      <td>1.4 (0.79-2.0) uM</td>\n",
       "      <td>Blood</td>\n",
       "      <td>7</td>\n",
       "      <td>Markerdb</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>Obesity</td>\n",
       "      <td>MSH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     marker     name      concentration sample  source_id source_name  \\\n",
       "0  L-Valine  obesity  1.4 (0.79-2.0) uM  Blood          7    Markerdb   \n",
       "\n",
       "  disease_id disease_name vocab  \n",
       "0   C0028754      Obesity   MSH  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicateRowsDF_marker = markerdb_disnet[markerdb_disnet.duplicated(['marker', 'disease_id'])]\n",
    "duplicateRowsDF_marker.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding the most repeated marker-disease associations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>name</th>\n",
       "      <th>disease_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>L-Leucine</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Alpha-ketoisovaleric acid</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3-Methyl-2-oxovaleric acid</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2-Hydroxy-3-methylbutyric acid</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>L-Valine</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Hydroxyisocaproic acid</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Mevalonic acid</td>\n",
       "      <td>mevalonic aciduria</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Lipoxin A4</td>\n",
       "      <td>coronary artery disease</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>L-Homocystine</td>\n",
       "      <td>homocystinuria</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Androstenedione</td>\n",
       "      <td>congenital adrenal hyperplasia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>L-Arginine</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>L-Alanine</td>\n",
       "      <td>lung cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Betaine</td>\n",
       "      <td>lung cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>trans-Aconitic acid</td>\n",
       "      <td>lung cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17-Hydroxyprogesterone</td>\n",
       "      <td>congenital adrenal hyperplasia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Ethanolamine</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Citric acid</td>\n",
       "      <td>maple syrup urine disease</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>Putrescine</td>\n",
       "      <td>pancreatic cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Glycolic acid</td>\n",
       "      <td>lung cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>D-Xylose</td>\n",
       "      <td>lung cancer</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             marker                            name  \\\n",
       "468                       L-Leucine       maple syrup urine disease   \n",
       "129       Alpha-ketoisovaleric acid       maple syrup urine disease   \n",
       "66       3-Methyl-2-oxovaleric acid       maple syrup urine disease   \n",
       "39   2-Hydroxy-3-methylbutyric acid       maple syrup urine disease   \n",
       "508                        L-Valine       maple syrup urine disease   \n",
       "368          Hydroxyisocaproic acid       maple syrup urine disease   \n",
       "558                  Mevalonic acid              mevalonic aciduria   \n",
       "528                      Lipoxin A4         coronary artery disease   \n",
       "454                   L-Homocystine                  homocystinuria   \n",
       "135                 Androstenedione  congenital adrenal hyperplasia   \n",
       "418                      L-Arginine       maple syrup urine disease   \n",
       "405                       L-Alanine                     lung cancer   \n",
       "159                         Betaine                     lung cancer   \n",
       "999             trans-Aconitic acid                     lung cancer   \n",
       "32           17-Hydroxyprogesterone  congenital adrenal hyperplasia   \n",
       "281                    Ethanolamine       maple syrup urine disease   \n",
       "194                     Citric acid       maple syrup urine disease   \n",
       "849                      Putrescine               pancreatic cancer   \n",
       "320                   Glycolic acid                     lung cancer   \n",
       "242                        D-Xylose                     lung cancer   \n",
       "\n",
       "     disease_id  \n",
       "468          20  \n",
       "129          20  \n",
       "66           20  \n",
       "39           20  \n",
       "508          20  \n",
       "368          20  \n",
       "558          18  \n",
       "528          12  \n",
       "454          10  \n",
       "135          10  \n",
       "418          10  \n",
       "405          10  \n",
       "159          10  \n",
       "999          10  \n",
       "32           10  \n",
       "281          10  \n",
       "194          10  \n",
       "849          10  \n",
       "320          10  \n",
       "242          10  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_disease = markerdb_disnet.groupby(['marker', 'name']).count()\n",
    "marker_disease = marker_disease.reset_index()\n",
    "marker_disease[['marker', 'name','disease_id']].sort_values(by=['disease_id'], ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#duplicateRowsDF_marker_1.query('name == \"obesity\" & marker == \"1-Methylhistidine\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Study of the relationships between the Biomarkers and DISNET features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "import json\n",
    "conn_param_file = 'C:/Users/end user/OneDrive/Desktop/UPM Master/CTB TFM/Datasets/DISNET_CONNECTION_correct.json'\n",
    "# Setting the connection to the database\n",
    "\n",
    "# The connection configuration is stored in a JSON file\n",
    "with open(conn_param_file, 'r') as f:\n",
    "    config = json.load(f) # The JSON file is translated to a pyhton dictionary\n",
    "\n",
    "# Stablishing the connection with the parameters in the dictionary\n",
    "cnx = mysql.connector.connect(**config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Querying DISNET to obtain the ICD-10 classification names and class range that correspond to each CUI code (disease_id) stored in DISNET**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>class_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0008354</td>\n",
       "      <td>Certain infectious and parasitic diseases</td>\n",
       "      <td>A00-B99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0178238</td>\n",
       "      <td>Certain infectious and parasitic diseases</td>\n",
       "      <td>A00-B99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0041466</td>\n",
       "      <td>Certain infectious and parasitic diseases</td>\n",
       "      <td>A00-B99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0030528</td>\n",
       "      <td>Certain infectious and parasitic diseases</td>\n",
       "      <td>A00-B99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0152491</td>\n",
       "      <td>Certain infectious and parasitic diseases</td>\n",
       "      <td>A00-B99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>C0013182</td>\n",
       "      <td>Injury, poisoning and certain other consequenc...</td>\n",
       "      <td>S00-T98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>C0041755</td>\n",
       "      <td>Injury, poisoning and certain other consequenc...</td>\n",
       "      <td>S00-T98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>C0085639</td>\n",
       "      <td>External causes of morbidity and mortality</td>\n",
       "      <td>V01-Y98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3616</th>\n",
       "      <td>C0019699</td>\n",
       "      <td>Factors influencing health status and contact ...</td>\n",
       "      <td>Z00-Z99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>C0037316</td>\n",
       "      <td>Factors influencing health status and contact ...</td>\n",
       "      <td>Z00-Z99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3618 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     disease_id                                         class_name class_range\n",
       "0      C0008354          Certain infectious and parasitic diseases     A00-B99\n",
       "1      C0178238          Certain infectious and parasitic diseases     A00-B99\n",
       "2      C0041466          Certain infectious and parasitic diseases     A00-B99\n",
       "3      C0030528          Certain infectious and parasitic diseases     A00-B99\n",
       "4      C0152491          Certain infectious and parasitic diseases     A00-B99\n",
       "...         ...                                                ...         ...\n",
       "3613   C0013182  Injury, poisoning and certain other consequenc...     S00-T98\n",
       "3614   C0041755  Injury, poisoning and certain other consequenc...     S00-T98\n",
       "3615   C0085639         External causes of morbidity and mortality     V01-Y98\n",
       "3616   C0019699  Factors influencing health status and contact ...     Z00-Z99\n",
       "3617   C0037316  Factors influencing health status and contact ...     Z00-Z99\n",
       "\n",
       "[3618 rows x 3 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_icd = \"\"\"\n",
    "             SELECT DISTINCT\n",
    "                d.disease_id, i.class_name, i.class_range\n",
    "             FROM\n",
    "                disnet_biolayer.disease d\n",
    "                    INNER JOIN\n",
    "                disnet_biolayer.has_code hc ON d.disease_id = hc.disease_id\n",
    "                    LEFT JOIN\n",
    "                disnet_biolayer.tmp_icd i ON 1 = 1\n",
    "             WHERE\n",
    "                hc.vocabulary = 'ICD10CM'\n",
    "                    AND SUBSTR(i.class_range, 1, 3) <= SUBSTR(hc.code_id, 1, 3)\n",
    "                    AND SUBSTR(i.class_range, 5, 3) >= SUBSTR(hc.code_id, 1, 3)\n",
    "\n",
    "            \"\"\"\n",
    "\n",
    "query_icd = pd.read_sql_query(query_icd, con = cnx)\n",
    "query_icd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparison between the distribution of DISNET genes, variants and pathways by ICD-10 diseases and the distribution of biomarkers by ICD-10 diseases.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We first generate a dataframe in which we group the disease_id and class_name columns of each DISNET disease to find out how many genes each grouping has associated with it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>score</th>\n",
       "      <th>sio_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>672</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>1280</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>1482</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>1811</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358204</th>\n",
       "      <td>C4540535</td>\n",
       "      <td>2778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>SIO_001122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358205</th>\n",
       "      <td>C4540536</td>\n",
       "      <td>2778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>SIO_001122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358206</th>\n",
       "      <td>C4540602</td>\n",
       "      <td>2778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>SIO_001122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358207</th>\n",
       "      <td>C4543926</td>\n",
       "      <td>3060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>SIO_001121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358208</th>\n",
       "      <td>C4545381</td>\n",
       "      <td>5156</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>SIO_001348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358209 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease_id  gene_id  source_id  score      sio_id\n",
       "0        C0000731       72          1   0.10  SIO_001121\n",
       "1        C0000731      672          1   0.10  SIO_001121\n",
       "2        C0000731     1280          1   0.10  SIO_001121\n",
       "3        C0000731     1482          1   0.10  SIO_001121\n",
       "4        C0000731     1811          1   0.10  SIO_001121\n",
       "...           ...      ...        ...    ...         ...\n",
       "358204   C4540535     2778          1   0.30  SIO_001122\n",
       "358205   C4540536     2778          1   0.30  SIO_001122\n",
       "358206   C4540602     2778          1   0.30  SIO_001122\n",
       "358207   C4543926     3060          1   0.03  SIO_001121\n",
       "358208   C4545381     5156          1   0.30  SIO_001348\n",
       "\n",
       "[358209 rows x 5 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_disease_gene = \"\"\"\n",
    "                         SELECT*\n",
    "                         FROM\n",
    "                             disnet_biolayer.disease_gene\n",
    "                        \"\"\"\n",
    "\n",
    "query_disease_gene = pd.read_sql_query(query_disease_gene, con = cnx)\n",
    "query_disease_gene "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging query_disease_gene and query_icd dataframes. Then, the resulting merged dataframe will be merged again with a df containing the icd10 short version names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>class_range</th>\n",
       "      <th>icd_class_name</th>\n",
       "      <th>class_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>25</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>27</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>238</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>338</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>348</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118041</th>\n",
       "      <td>C1691779</td>\n",
       "      <td>2707</td>\n",
       "      <td>H60-H95</td>\n",
       "      <td>Diseases of the ear and mastoid process</td>\n",
       "      <td>Ear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118042</th>\n",
       "      <td>C1691779</td>\n",
       "      <td>4036</td>\n",
       "      <td>H60-H95</td>\n",
       "      <td>Diseases of the ear and mastoid process</td>\n",
       "      <td>Ear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118043</th>\n",
       "      <td>C1691779</td>\n",
       "      <td>5172</td>\n",
       "      <td>H60-H95</td>\n",
       "      <td>Diseases of the ear and mastoid process</td>\n",
       "      <td>Ear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118044</th>\n",
       "      <td>C1691779</td>\n",
       "      <td>8772</td>\n",
       "      <td>H60-H95</td>\n",
       "      <td>Diseases of the ear and mastoid process</td>\n",
       "      <td>Ear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118045</th>\n",
       "      <td>C1691779</td>\n",
       "      <td>10265</td>\n",
       "      <td>H60-H95</td>\n",
       "      <td>Diseases of the ear and mastoid process</td>\n",
       "      <td>Ear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118046 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease_id  gene_id class_range  \\\n",
       "0        C0000744       25     E00-E90   \n",
       "1        C0000744       27     E00-E90   \n",
       "2        C0000744      238     E00-E90   \n",
       "3        C0000744      338     E00-E90   \n",
       "4        C0000744      348     E00-E90   \n",
       "...           ...      ...         ...   \n",
       "118041   C1691779     2707     H60-H95   \n",
       "118042   C1691779     4036     H60-H95   \n",
       "118043   C1691779     5172     H60-H95   \n",
       "118044   C1691779     8772     H60-H95   \n",
       "118045   C1691779    10265     H60-H95   \n",
       "\n",
       "                                       icd_class_name class_name  \n",
       "0       Endocrine, nutritional and metabolic diseases  Metabolic  \n",
       "1       Endocrine, nutritional and metabolic diseases  Metabolic  \n",
       "2       Endocrine, nutritional and metabolic diseases  Metabolic  \n",
       "3       Endocrine, nutritional and metabolic diseases  Metabolic  \n",
       "4       Endocrine, nutritional and metabolic diseases  Metabolic  \n",
       "...                                               ...        ...  \n",
       "118041        Diseases of the ear and mastoid process        Ear  \n",
       "118042        Diseases of the ear and mastoid process        Ear  \n",
       "118043        Diseases of the ear and mastoid process        Ear  \n",
       "118044        Diseases of the ear and mastoid process        Ear  \n",
       "118045        Diseases of the ear and mastoid process        Ear  \n",
       "\n",
       "[118046 rows x 5 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_gene_icd= pd.merge(query_disease_gene, query_icd.drop(['class_name'], axis=1), on='disease_id')\n",
    "disease_gene_icd = disease_gene_icd.drop(['source_id', 'sio_id','score'], axis=1)\n",
    "icd_10_short = pd.read_csv('icd10_links.csv')\n",
    "disease_gene_icd= pd.merge(disease_gene_icd, icd_10_short, on='class_range')\n",
    "disease_gene_icd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>num_feature_disease</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002940</td>\n",
       "      <td>56</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002962</td>\n",
       "      <td>54</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002963</td>\n",
       "      <td>1</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002965</td>\n",
       "      <td>19</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0003486</td>\n",
       "      <td>95</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0702166</td>\n",
       "      <td>44</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C1112570</td>\n",
       "      <td>4</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C1260874</td>\n",
       "      <td>1</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C2936846</td>\n",
       "      <td>1</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2800</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C4083212</td>\n",
       "      <td>19</td>\n",
       "      <td>genes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2801 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class_name disease_id  num_feature_disease feature\n",
       "0     Circulatory   C0002940                   56   genes\n",
       "1     Circulatory   C0002962                   54   genes\n",
       "2     Circulatory   C0002963                    1   genes\n",
       "3     Circulatory   C0002965                   19   genes\n",
       "4     Circulatory   C0003486                   95   genes\n",
       "...           ...        ...                  ...     ...\n",
       "2796         Skin   C0702166                   44   genes\n",
       "2797         Skin   C1112570                    4   genes\n",
       "2798         Skin   C1260874                    1   genes\n",
       "2799         Skin   C2936846                    1   genes\n",
       "2800         Skin   C4083212                   19   genes\n",
       "\n",
       "[2801 rows x 4 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_gene_icd_test = disease_gene_icd[['disease_id', 'class_name', 'gene_id']]\n",
    "#disease_gene_icd_test['gene_id'] = disease_gene_icd_test['gene_id'].apply(str)\n",
    "disease_gene_icd_test = disease_gene_icd_test.groupby(['class_name', 'disease_id']).count()#.agg(num_feature_disease=('gene_id', sum))\n",
    "#disease_gene_icd_test\n",
    "disease_gene_icd_test = disease_gene_icd_test.reset_index()\n",
    "disease_gene_icd_test = disease_gene_icd_test.rename(columns={'gene_id': 'num_feature_disease'})\n",
    "disease_gene_icd_test['feature'] = 'genes'\n",
    "disease_gene_icd_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now that we have a dataframe with the number of genes that each class_name and disease_id pair has, we will repeat the same process for variants and pathways.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Variants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>variant_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0000737</td>\n",
       "      <td>rs1057518806</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0000737</td>\n",
       "      <td>rs1057518886</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>rs146064714</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>rs199422219</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0000744</td>\n",
       "      <td>rs199422220</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210493</th>\n",
       "      <td>C4540536</td>\n",
       "      <td>rs148033592</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210494</th>\n",
       "      <td>C4540536</td>\n",
       "      <td>rs61749698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210495</th>\n",
       "      <td>C4540602</td>\n",
       "      <td>rs148033592</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210496</th>\n",
       "      <td>C4540602</td>\n",
       "      <td>rs61749698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210497</th>\n",
       "      <td>C4543822</td>\n",
       "      <td>rs1044396</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210498 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease_id    variant_id  source_id  score\n",
       "0        C0000737  rs1057518806          1   0.70\n",
       "1        C0000737  rs1057518886          1   0.70\n",
       "2        C0000744   rs146064714          1   0.70\n",
       "3        C0000744   rs199422219          1   0.70\n",
       "4        C0000744   rs199422220          1   0.80\n",
       "...           ...           ...        ...    ...\n",
       "210493   C4540536   rs148033592          1   0.70\n",
       "210494   C4540536    rs61749698          1   0.70\n",
       "210495   C4540602   rs148033592          1   0.70\n",
       "210496   C4540602    rs61749698          1   0.70\n",
       "210497   C4543822     rs1044396          1   0.01\n",
       "\n",
       "[210498 rows x 4 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_variants = \"\"\"\n",
    "                     SELECT*\n",
    "                     FROM\n",
    "                         disnet_biolayer.disease_variant\n",
    "                    \"\"\"\n",
    "\n",
    "query_variants = pd.read_sql_query(query_variants, con = cnx)\n",
    "query_variants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>num_feature_disease</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002940</td>\n",
       "      <td>7</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002962</td>\n",
       "      <td>64</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002963</td>\n",
       "      <td>2</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002965</td>\n",
       "      <td>4</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0003486</td>\n",
       "      <td>9</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0406317</td>\n",
       "      <td>1</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0409974</td>\n",
       "      <td>17</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0477474</td>\n",
       "      <td>1</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0702166</td>\n",
       "      <td>8</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C4083212</td>\n",
       "      <td>2</td>\n",
       "      <td>variants</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1628 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class_name disease_id  num_feature_disease   feature\n",
       "0     Circulatory   C0002940                    7  variants\n",
       "1     Circulatory   C0002962                   64  variants\n",
       "2     Circulatory   C0002963                    2  variants\n",
       "3     Circulatory   C0002965                    4  variants\n",
       "4     Circulatory   C0003486                    9  variants\n",
       "...           ...        ...                  ...       ...\n",
       "1623         Skin   C0406317                    1  variants\n",
       "1624         Skin   C0409974                   17  variants\n",
       "1625         Skin   C0477474                    1  variants\n",
       "1626         Skin   C0702166                    8  variants\n",
       "1627         Skin   C4083212                    2  variants\n",
       "\n",
       "[1628 rows x 4 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_variant_icd= pd.merge(query_variants, query_icd.drop(['class_name'], axis=1), on='disease_id')# query_disease_variant es un df de la tabla #disease_variant y query_icd un df de la consulta icd\n",
    "disease_variant_icd = disease_variant_icd.drop(['source_id','score'], axis=1)\n",
    "icd_10_short = pd.read_csv('icd10_links.csv')\n",
    "disease_variant_icd= pd.merge(disease_variant_icd, icd_10_short, on='class_range')\n",
    "disease_variant_icd\n",
    "\n",
    "disease_variant_icd_test = disease_variant_icd[['disease_id', 'class_name', 'variant_id']]\n",
    "disease_variant_icd_test = disease_variant_icd_test.groupby(['class_name', 'disease_id']).count()\n",
    "disease_variant_icd_test = disease_variant_icd_test.reset_index()\n",
    "disease_variant_icd_test = disease_variant_icd_test.rename(columns={'variant_id': 'num_feature_disease'})\n",
    "disease_variant_icd_test['feature'] = 'variants'\n",
    "disease_variant_icd_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pathways**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_id</th>\n",
       "      <th>pathway_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>WP117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>WP138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>WP15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>WP1533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0000731</td>\n",
       "      <td>WP1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400696</th>\n",
       "      <td>C4540602</td>\n",
       "      <td>WP734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400697</th>\n",
       "      <td>C4545381</td>\n",
       "      <td>WP306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400698</th>\n",
       "      <td>C4545381</td>\n",
       "      <td>WP322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400699</th>\n",
       "      <td>C4545381</td>\n",
       "      <td>WP3611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400700</th>\n",
       "      <td>C4545381</td>\n",
       "      <td>WP51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400701 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disease_id pathway_id\n",
       "0        C0000731      WP117\n",
       "1        C0000731      WP138\n",
       "2        C0000731       WP15\n",
       "3        C0000731     WP1533\n",
       "4        C0000731     WP1544\n",
       "...           ...        ...\n",
       "400696   C4540602      WP734\n",
       "400697   C4545381      WP306\n",
       "400698   C4545381      WP322\n",
       "400699   C4545381     WP3611\n",
       "400700   C4545381       WP51\n",
       "\n",
       "[400701 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_pathways = \"\"\"\n",
    "                 SELECT DISTINCT\n",
    "                 dg.disease_id,\n",
    "                 gp.pathway_id\n",
    "                 FROM\n",
    "                     disnet_biolayer.disease_gene dg\n",
    "                         INNER JOIN\n",
    "                     disnet_biolayer.gene g ON dg.gene_id = g.gene_id\n",
    "                         INNER JOIN\n",
    "                     disnet_biolayer.gene_pathway gp ON g.gene_id = gp.gene_id\n",
    "                \"\"\"\n",
    "\n",
    "query_pathways = pd.read_sql_query(query_pathways, con = cnx)\n",
    "query_pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>num_feature_disease</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002940</td>\n",
       "      <td>120</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002962</td>\n",
       "      <td>96</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002963</td>\n",
       "      <td>8</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0002965</td>\n",
       "      <td>79</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0003486</td>\n",
       "      <td>139</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0549567</td>\n",
       "      <td>9</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0600336</td>\n",
       "      <td>1</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0702166</td>\n",
       "      <td>81</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C1112570</td>\n",
       "      <td>4</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C4083212</td>\n",
       "      <td>31</td>\n",
       "      <td>pathway</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2428 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class_name disease_id  num_feature_disease  feature\n",
       "0     Circulatory   C0002940                  120  pathway\n",
       "1     Circulatory   C0002962                   96  pathway\n",
       "2     Circulatory   C0002963                    8  pathway\n",
       "3     Circulatory   C0002965                   79  pathway\n",
       "4     Circulatory   C0003486                  139  pathway\n",
       "...           ...        ...                  ...      ...\n",
       "2423         Skin   C0549567                    9  pathway\n",
       "2424         Skin   C0600336                    1  pathway\n",
       "2425         Skin   C0702166                   81  pathway\n",
       "2426         Skin   C1112570                    4  pathway\n",
       "2427         Skin   C4083212                   31  pathway\n",
       "\n",
       "[2428 rows x 4 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_pathways_icd= pd.merge(query_pathways, query_icd.drop(['class_name'], axis=1), on='disease_id')# query_disease_pathways es un df de la tabla #disease_pathways y query_icd un df de la consulta icd\n",
    "#disease_pathways_icd #= disease_pathways_icd.drop(['source_id','score'], axis=1)\n",
    "icd_10_short = pd.read_csv('icd10_links.csv')\n",
    "disease_pathways_icd= pd.merge(disease_pathways_icd, icd_10_short, on='class_range')\n",
    "disease_pathways_icd\n",
    "\n",
    "disease_pathways_icd_test = disease_pathways_icd[['disease_id', 'class_name', 'pathway_id']]\n",
    "disease_pathways_icd_test = disease_pathways_icd_test.groupby(['class_name', 'disease_id']).count()\n",
    "disease_pathways_icd_test = disease_pathways_icd_test.reset_index()\n",
    "disease_pathways_icd_test = disease_pathways_icd_test.rename(columns={'pathway_id': 'num_feature_disease'})\n",
    "disease_pathways_icd_test['feature'] = 'pathway'\n",
    "disease_pathways_icd_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we process df_disease_marker dataframe where the biomarkers and the diseases associated to each of them is stored. We will add the ICD class_name to each disease_id**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>marker</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>class_range</th>\n",
       "      <th>icd_class_name</th>\n",
       "      <th>class_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Methylhistidine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(R)-3-Hydroxybutyric acid</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L-Carnitine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L-Threonine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ornithine</td>\n",
       "      <td>C0028754</td>\n",
       "      <td>E00-E90</td>\n",
       "      <td>Endocrine, nutritional and metabolic diseases</td>\n",
       "      <td>Metabolic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>Theophylline</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>J00-J99</td>\n",
       "      <td>Diseases of the respiratory system</td>\n",
       "      <td>Respiratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>Theobromine</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>J00-J99</td>\n",
       "      <td>Diseases of the respiratory system</td>\n",
       "      <td>Respiratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>11b-PGF2a</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>J00-J99</td>\n",
       "      <td>Diseases of the respiratory system</td>\n",
       "      <td>Respiratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>1,7-Dimethyluric acid</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>J00-J99</td>\n",
       "      <td>Diseases of the respiratory system</td>\n",
       "      <td>Respiratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>5-HETE</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>J00-J99</td>\n",
       "      <td>Diseases of the respiratory system</td>\n",
       "      <td>Respiratory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1717 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         marker disease_id class_range  \\\n",
       "0             1-Methylhistidine   C0028754     E00-E90   \n",
       "1     (R)-3-Hydroxybutyric acid   C0028754     E00-E90   \n",
       "2                   L-Carnitine   C0028754     E00-E90   \n",
       "3                   L-Threonine   C0028754     E00-E90   \n",
       "4                     Ornithine   C0028754     E00-E90   \n",
       "...                         ...        ...         ...   \n",
       "1712               Theophylline   C1319018     J00-J99   \n",
       "1713                Theobromine   C1319018     J00-J99   \n",
       "1714                  11b-PGF2a   C1319018     J00-J99   \n",
       "1715      1,7-Dimethyluric acid   C1319018     J00-J99   \n",
       "1716                     5-HETE   C1319018     J00-J99   \n",
       "\n",
       "                                     icd_class_name   class_name  \n",
       "0     Endocrine, nutritional and metabolic diseases    Metabolic  \n",
       "1     Endocrine, nutritional and metabolic diseases    Metabolic  \n",
       "2     Endocrine, nutritional and metabolic diseases    Metabolic  \n",
       "3     Endocrine, nutritional and metabolic diseases    Metabolic  \n",
       "4     Endocrine, nutritional and metabolic diseases    Metabolic  \n",
       "...                                             ...          ...  \n",
       "1712             Diseases of the respiratory system  Respiratory  \n",
       "1713             Diseases of the respiratory system  Respiratory  \n",
       "1714             Diseases of the respiratory system  Respiratory  \n",
       "1715             Diseases of the respiratory system  Respiratory  \n",
       "1716             Diseases of the respiratory system  Respiratory  \n",
       "\n",
       "[1717 rows x 5 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disease_marker_1 = df_disease_marker[['marker', 'disease_id']]\n",
    "icd_marker= pd.merge(df_disease_marker_1, query_icd.drop(['class_name'], axis=1), on='disease_id')\n",
    "icd_marker\n",
    "# disease_gene_icd = disease_gene_icd.drop(['source_id', 'sio_id','score'], axis=1)\n",
    "icd_10_short = pd.read_csv('icd10_links.csv')\n",
    "icd_marker= pd.merge(icd_marker, icd_10_short, on='class_range')\n",
    "icd_marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_name</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>num_feature_disease</th>\n",
       "      <th>class_range</th>\n",
       "      <th>icd_class_name</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0004153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0010054</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0010068</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0010072</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Circulatory</td>\n",
       "      <td>C0020538</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Respiratory</td>\n",
       "      <td>C0264413</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Respiratory</td>\n",
       "      <td>C1260881</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Respiratory</td>\n",
       "      <td>C1319018</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0013595</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Skin</td>\n",
       "      <td>C0042900</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>biomarker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class_name disease_id  num_feature_disease  class_range  icd_class_name  \\\n",
       "0    Circulatory   C0004153                    1            1               1   \n",
       "1    Circulatory   C0010054                    1            1               1   \n",
       "2    Circulatory   C0010068                    1            1               1   \n",
       "3    Circulatory   C0010072                    3            3               3   \n",
       "4    Circulatory   C0020538                    9            9               9   \n",
       "..           ...        ...                  ...          ...             ...   \n",
       "175  Respiratory   C0264413                    8            8               8   \n",
       "176  Respiratory   C1260881                    8            8               8   \n",
       "177  Respiratory   C1319018                    8            8               8   \n",
       "178         Skin   C0013595                    2            2               2   \n",
       "179         Skin   C0042900                    1            1               1   \n",
       "\n",
       "       feature  \n",
       "0    biomarker  \n",
       "1    biomarker  \n",
       "2    biomarker  \n",
       "3    biomarker  \n",
       "4    biomarker  \n",
       "..         ...  \n",
       "175  biomarker  \n",
       "176  biomarker  \n",
       "177  biomarker  \n",
       "178  biomarker  \n",
       "179  biomarker  \n",
       "\n",
       "[180 rows x 6 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icd_marker_1 = icd_marker[['disease_id', 'class_name', 'marker']]\n",
    "\n",
    "icd_marker_2 = icd_marker.groupby(['class_name', 'disease_id']).count()#agg(num_feature_disease=('marker_id', sum))\n",
    "icd_marker_2 = icd_marker_2.reset_index()\n",
    "# icd_marker_1 = icd_marker.reset_index()\n",
    "icd_marker_2 = icd_marker_2.rename(columns={'marker': 'num_feature_disease'})\n",
    "icd_marker_2['feature'] = 'biomarker'\n",
    "icd_marker_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting the biomarker and feature distribution per ICD-10 disease group**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Biomarker and gene distribution per ICD10 disease group')]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = pd.concat([icd_marker_2, disease_gene_icd_test])\n",
    "fig, ax = plot.subplots(figsize = (9, 9)) \n",
    "plot.xlim(0, 100)\n",
    "ax = sns.boxplot(data=df_to_plot, x='num_feature_disease', y='class_name', hue='feature')\n",
    "ax.set(xlabel='num_feature_disease', ylabel='class_name')\n",
    "ax.set(title='Biomarker and gene distribution per ICD10 disease group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Variant and gene distribution per ICD10 disease group')]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2j8bV19fr0SQiIiI5VvLJnJkZ8Btgubsf4O4HA+cB7u6npth+tJ6tdzKQUTKXq1jGjx8PFZXDbtc3YZe0ZonItYULFzJx4kSNyomIiIyCcnho8LHANne/or/A3deYWb2ZPeTuh5rZGcDxwARgopmdCLQCDYADF7n7TWa22d0nAZjZqcAJ7n5GYmdm9hngLGAnIAp8EpgBnAi818wWAqcAOwNXADXAP4Az3f0lM1sO/A04GvhzGNtb3H2bme0CPAhMc/dtGR2F3jjVjy7Zvljx2qtY37YdtolGN9Hc3JxR04PpfwBxutOItba25qRfGVs0BZ2IyNDKIZk7FFiZxnbvBKa7+4tm9m3gFXc/DMDMds+gv5vd/adhvRbgv9y91cxuA2539xvDdQ8C89z9TjP7BnABcE7Yxm7u/t5wu3qCRPMW4DTgplSJnJmdRZBEUldXN2Dd3nvvHc7Nutf2smh0E751K3WTepNaep2t61dksLuD694UjAZujT+bk/ZEkm3YPPyIs4jIWFcOyVy6lrp7/+SkswkSJwDc/aUM2jk0TOJ2AyYBf0jewMx2JUjY7gyL2oBfJ2xyQ8Lrq4AvEyRznwI+k6pTd78SuBKgoaFhwHQPV1111Q7bNzc3s3X9Cs6b+Wo6+zQiF6/aBWBU+5Cxrf8zJiIigyv5a+aAdcCRaWzXnfDaCE6vJkssmzBIO4uBs8NRvYuG2C6tWNz9bqDezN4LVLr7Q5k21traqlOYIjJm6W+g9IvFYsyfP59YLFboUPKqHJK5PwPjw2vZADCzo4CpQ9T5I3B2wvb9p1k3mtlBZlYBfGiQujsDz5rZOOD0hPJN4Trc/RXgJTN7d7juk8CdDK4duB74+RDbDCoajWqaLBEZs/Q3UPq1tbWxdu1a2tvbCx1KXpV8MufuTpB4zQkfTbIOuBB4ZohqLcDuZvaQmT1AcBMFwFeB2wkSxMEuBDsfuBdYCjyaUP5L4H/MbLWZHQA0Ad8Nr52bAXxjiHiuBXYnSOhEREQkQ7FYjI6ODtydjo6OMTU6VxbXzLn7M8BHU6w6NFy/mOD0aP/2mwmSreR2bgR2eDadu1+Y8Pp/gf9Nsc3d7Phoknek2G5WijiPAW5095dTrBtWV1cXPT09A+5SjUaj7LSt5HN1GeM2bqng9Wg0Z3dgS3mKRqN5f4amFJ+2tjb6+voA6O3tpb29nQULFhQ4qvzQt32BmVkrcCnwzULHIiIiUqqWLVtGPB4HIB6Ps3Tp0gJHlD9lMTJXytw96wdo9T/nbdGiRdvL+u9mFSlle9X0MX5qZMBnWySZRm4FYPbs2SxZsoR4PE5VVRVz5swpdEh5o5E5ERERKXlNTU3B9JZAZWUlc+fOLXBE+aNkrgxEIhHNeSoiY5b+BgpAbW0tjY2NmBmNjY3U1tYWOqS80WnWMjDYVEcbNleO6kNX14czQOjBrjJaNmyuZFqhg5Cip+nepF9TUxOdnZ1jalQOlMyVrXz8L3ViODfr+DTmZhUZiWnk57MsIuWhtraWyy+/vNBh5J2SuTKl/6mKiIiMDbpmTkRERKSEKZkTERERKWFK5kRERERKmK6ZG4NaW1sznpS6K7zZYUqJ3ewQiUR0/aCIiJQ1JXNjUDQaZc1Dj9BbMzntOpVbXgHgua2l85Gp3PJioUMQEREZdaXzzSw51VszmZ4Dj0t7++pHlwBkVKfQ+mMWEREpZ0rmylRrayswth9RUvHaq3R1xQsdhoiIyKhSMlemMr0mrhxZ3zZ6enoKHYaIiMio0t2sIiIiIiVMI3Nlqquri56eHpqbm3dYF41GqXjdCxCViIiI5JpG5kRERERKmEbmylT/8+AWLVq0w7rm5mZW/nNjvkMSERGRUVAWI3Nm9m9m9ksz+4eZPWxmS8zsLQWOaYmZ7Rb+fCHNOptHOy4REREpLyWfzJmZAb8Blrv7Ae5+MHAesFch43L349z9ZWA3IK1kLpcikQiRSCTf3RYVrxhHdXV1ocMQEREZVeVwmvVYYJu7X9Ff4O5rLPBd4IOAAy3ufoOZzQIuBF4ADgVWAp9wdzez44DLwnWrgDe7+wlmNhFoBQ4jOGYXuvutZnYGcCJQAxwA/MbdvwxgZp1AA3ApcICZrQGWAhcBtwK7A+OAhe5+a64Pylh+vly/vgm7MGVKQXN6ERGRUVcOyVx/Qpbsw8AM4HBgD+B+M7srXHcEcAjwDHA3cLSZrQD+D3iPuz9pZtcntPU14M/ufqaZ7QbcZ2bLwnUzwva2Ao+ZWau7P5VQ96vAoe4+A8DMqoAPufurZrYH8Hczu83dc3p76VDzr0ajUSq7tzBp1TXpN9i7DSCzOoXWG9dDg0VEpOyVQzI3mGOA6929F9hoZncCRwGvAve5+9MA4YhZPbAZ+Ke7PxnWvx44K3z9fuBEMzs3XJ4A1IWv/+Tur4RtPQxMBRKTuWQGXGxm7wH6gCkEp4SfG2pnzOys/njq6uqG2hQIErYn1q2mblLvDuv2G0dw8pfXh21ne3uvBB+VyM5b0q5TaBs2VxY6BBERkVFXDsncOuDUFOU2RJ2tCa97CY7DUNsbcIq7Pzag0Oztg7Q1lNOBNwFHuvu28HTshGHq4O5XAlcCNDQ0pDWKVzepl/NmvprOpsO6eNUuADlrLx8uXrUL48O7ekVERMpVyd8AAfwZGG9mn+kvMLOjgJeAj5lZpZm9CXgPcN8Q7TwKvNnM6sPljyWs+wMwL7zZAjM7IoP4NgE7JyzvCvwrTOSOJRjJy0pra+v2uVhFRlO2n7VYLMb8+fOJxWJDbheNRjn++OOHnZYuVXvp1s0mvnRjTlWWTR+lqJz3t9j3LVV8hYy52I9XKSv5ZC681uxDwJzw0STrCG5wuA54EHiAIOH7srsPeirT3XsI7jrtMLO/AhuBV8LV3yS4WeFBM3soXE43vhhwt5k9FN6QcS3QEF6jdzpBEpmVaDSquVglL7L9rLW1tbF27Vra29uH3K6lpYXu7m5aWloybi/dutnEl27Mqcqy6aMUlfP+Fvu+pYqvkDEX+/EqZSWfzAG4+zPu/tHw0SSHuPvx7v6Eu/+Pux/q7oe5+w3htsvd/YSEume7++Jw8Q53PxB4N8GpzxXhNj3u/tmwnUP767v7Ync/O6GtE9x9efi63t1fCF//Z1jvf9z9BXd/p7s3uPun3f0gd+8Mt5s0yodKpGBisRgdHR24Ox0dHYP+7zwajdLZ2QlAZ2fnoMljqvbSrZtNfOnGnKosmz5KUTnvb7HvW6r4ChlzsR+vUlcO18zl0mfMrAnYCVhNcHdr0Us1D2s0GmWnbWWRq4/Yxi0VvB6NppyfVkYmGo2O+Nl9bW1t9PX1AdDb20t7ezsLFizYYbvkEbWWlhYWL16cVnsPPPBAWnWziS+VVDGn2mb69Okj7qMUZXNMi12x71uq+Ny9YDEX+/EqdWP72z6Ju//A3We4+8Hufrq7l86tmyJFbtmyZcTjwaNi4vE4S5cuTbld/2jWYMtDtZdu3WziSyVVv6nKsumjFJXz/hb7vqWKr5AxF/vxKnUamSsDqeZhbW5uZuv6FYUKqSjsVdPH+KmRlPPTyshkM8o5e/ZslixZQjwep6qqijlz5qTcrr6+fkAiVF9fn3Z7DzzwQFp1s4kvk5iTy6ZPnz7iPkpRNse02BX7vqWKz90LFnOxH69Sp5G5MqCpuyRfsvmsNTU1UVER/MmprKxk7ty5KbdbuHDhkMtDtZdu3WziSzfmVGXZ9FGKynl/i33fUsVXyJiL/XiVOiVzZWDevHmavkvyIpvPWm1tLY2NjZgZjY2N1NbWptwuEolsH9mqr68fNHlM1V66dbOJL92YU5Vl00cpKuf9LfZ9SxVfIWMu9uNV6nSatYxt2Fy5/WG/2Vq/KZhNIVft5cOGzZVMK3QQMkBTUxOdnZ3D/q984cKFNDc3Dzuylqq9dOtmE1+6Macqy6aPUlTO+1vs+5YqvkLGXOzHq5RZjqcElTxoaGjwFSuGvh5uqLlZR6Krqwt44/q8UhGJRDRqKSIiRc3MVrp7w0jra2SuTCmBERERGRt0zZyIiIhICVMyJyIiIlLClMyJiIiIlDBdMzeGZXuTxGA3ReimAxERkfxRMjeGRaNR1jz0CL01k0dUv3LLKwA8t7UqoezFnMQmIiIi6VEyN8b11kym58DjRlS3+tElAAPq95eJiIhIfuiauTLQ2tpKa2trocMAoOK1V7effi0VsViM+fPnE4vFCh2KiEjO6G/b2KFkrgxEo9GcPiA4G9a3jZ6enkKHkZG2tjbWrl1Le3t7oUMREckZ/W0bO5TMyZgWi8Xo6OjA3eno6ND/YEWkLOhv29iia+bKQFdXFz09PTQ3N2dULxqNUvH62J7Ora2tjb6+PgB6e3tpb29nwYIFBY5KRCQ7+ts2tmhkTsa0ZcuWEY/HAYjH4yxdurTAEYmIZE9/28YWjcyVgf7nvC1atCijes3Nzaz858bRCKlkzJ49myVLlhCPx6mqqmLOnDmFDklEJGv62za2lP3InJn1mtkaM1tnZg+Y2ZfMrCJc12Bml+e4vzPMbJ+E5avM7OBc9iG509TUREVF8GtQWVnJ3LlzCxyRiEj29LdtbCn7ZA7ocfcZ7n4IMAc4DrgAwN1XuPv8HPd3BrA9mXP3T7v7wznuY4BIJEIkEhnNLtLmFeOorq4udBhpq62tpbGxETOjsbGR2traQockIpI1/W0bW8bUaVZ3/5eZnQXcb2YXAu8FznX3E8zsTcB1QC1wP9AIHOnuL5jZJ4D5wE7AvcAXwiZ/BjQADlwNPBUuX2tmPcA7gd8D5wJHAfu7+5chGMEL25+Xqn137013v7KZOqtyy4sjftBv5Zbg7qjE+tYX32F6r2LX1NREZ2en/ucqImVFf9vGDnMv77sZzWyzu09KKnsJOBA4iDeSuR8BXe5+iZk1EiRhbwp/vgN82N23mdlPgL8D64BL3X1O2OZu7v6ymS0P21wRli8nSObWA/e4eyQs/z3wLSCWqn13H/TBQA0NDb5ixYoh9zudeVf774JNtnXrVgDGjx8/ZP3Btquuri5IQqc5YUVEpBSZ2Up3bxhp/TE1MpfAUpQdA3wIwN07woQP4H3AkQSjeQDVwL+A3wJvNrNW4HfAH4fq0N2fN7N/mtk7gCeAtwJ3A18cpP2BAQcjimcB1NXVDbuD0WiUJ9atpm7S4AN8ewCM27F8/WuVAOw3bpiH/26vm7Rd/GW2rn922BhzacPmyrz2JyIiUizGXDJnZm8GegkSpoMSVw1WBWhz9/+Xoq3DgQ8QJGQfBc4cpvsbwu0eBX7j7m5BBpey/UTufiVwJQQjc8P0A0DdpF7Om/lqOpsOcPGqXQBGVLdQ+mOW/IvFYlx00UVccMEFI7ouJ9366W4XjUZpbm5m0aJFo3Ytabb7LLlVqPdDn4OAjkPhjYUbILYLr4u7AviR73h++a8EiRZm9n5g97D8T8CpZrZnuG6ymU01sz2ACne/CTgfmBluvwnYeZAQbgZOBj5OkNgN2n4m+1VMc7NKeUv1Wct2yqB066e7XUtLC93d3bS0tIwonnRomqTiUqj3Q5+DgI5D4Y2FZK66/9EkwDKC06EXpdjuIuD9ZrYK+CDwLLApvBN1IfBHM3sQWArsDUwBlpvZGmAx0D+ythi4IuxzwG2d7v4S8DAw1d3vC8sGaz9txTQ3q5S35M9atlMGpVs/3e2i0SidnZ0AdHZ2jsrvhaZJKi6Fej/0OQjoOBSHsj/N6u6DXkzl7suB5eHiK8AH3D1uZu8EjnX3reF2N/DGSFqimckF4UjdTQlFs5LWn5CizmDtpyXVdF7RaJSdto2FXD2wcUsFr4en12T0RKPRAY+eyXbKoHTrp7td8mhcS0sLixcvTjueXMYs+VGo90Ofg4COQ3EYO9/2w6sjuAnhAeBy4DMFjkek6GU7ZVC69dPdrn9UbrDlXNA0ScWlUO+HPgcBHYfiUPYjc+ly9yeAIwodx0ikms6rubmZreuHfnxJOdmrpo/xUyMZT2kmmUke+cx2yqB066e7XX19/YAErr6+PqN4chmz5Eeh3g99DgI6DsVBI3MiMmLZThmUbv10t1u4cOGQy7mgaZKKS6HeD30OAjoOxUHJXBkopum8pLwlf9aynTIo3frpbheJRLaPxtXX14/K74WmSSouhXo/9DkI6DgUB51mLQOa9UDyJdVnLdspg9Ktn+52CxcupLm5eVRG5TKNRfKjUO+HPgcBHYfCK/vpvMpROtN5NTc3DzsDxGDWbwpuAJ66c+Z1C2XD5kqmHXKErpkTEZGSo+m8JKVsTi9N7OoCYHwB5lcdqWlkt88iIiKlSslcmdKpVxERkbFBN0CIiIiIlDAlcyIiIiIlTMmciIiISAnTNXNjSGtr66hMPC7Fq6uri912242rrrqq0KGIiMgoUTI3hkSjUdY89Ai9NZMLHYrkSeWmGD09PYUOQ0RERpGSuTGmt2YyPQceV+gwJE8mrbqm0CGIiMgoUzI3hnR1dVHx2pZChyEyprW2tgJ6fJCI5I6SuTGkp6cH69tW6DBExjRdtyoiuaa7WUVERERKmEbmRMpZXy89PT00NzcXOhIJRaNRqqurCx2GiJQRjcyJiIiIlDCNzImUs4pKqifsxKJFiwodiYQ0SioiuVY0I3NmtpeZXWdm/zSzlWZ2j5l9aIRtXWVmB4evz8txnJ8zs7m5bDNfqqur8YpxhQ5DZEyLRCJEIpFChyEiZaQoRubMzIBbgDZ3/8+wbCpw4kjac/dPJyyeB1ycbYxhTFXufkWO2qp0995ctJWuKVOm8NzWjfnsUkSS6JEkIpJrRZHMAf8OvJ6YKLn7eqDVzCqBS4FZwHjgx+7+f2Y2C7gQeAE4FFgJfMLd3cyWA+cCpwLVZrYGWOfup5vZl4Azw26ucvcfmlk9cLu7HwpgZucCk9z9wrCtvwFHA7eZ2c7AZnf/XrjuXuBYYDfgv9z9L2F71wATw37Odve/hTFfADwLzDCzm4AX3H1R2O+3gI3ufnkmBy/dabqi0SiV3VuGf5BsX5hjVlRmEoYMwivG0Tdhl8J03hsHdipM3yIikhfFkswdAqwaZN1/Aa+4+1FmNh6428z+GK47Iqz7DHA3QcL11/6K7v5VMzvb3WcAmNmRwKeAtwMG3GtmdwIvDRPfbu7+3rCNC5PWVbn728zsOIJEbTbwL2COu79mZtOA64GGcPu3AYe6+5Nh0nczsMjMKoDTwvU7MLOzgLMA6urqBqyLRqM8sW41dZOGHujbbxxBysnrQ263flOQxE3deeuQ28nwNmyuxHbaicib9ypI/11dcXbbbbeC9C0iIvlRLMncAGb2Y+AYgqxjPTDdzE4NV+8KTAvX3efuT4d11gD1JCRzKRwD/Mbdu8M6NwPvBm4bJqQbhlh3c/jvyrB/gHHAj8xsBtALvCVh+/vc/UkAd+80s5iZHQHsBax291iqTtz9SuBKgIaGBk9eXzepl/NmvjrMbqTn4lXBKFKu2hvLLl61C+OnRnQDgoiIjJpiuQFiHTCzf8Hdvwi8D3gTwQjaPHefEf7s7+79I3OJQ0e9DJ+c2iDlcQYeiwlJ67uHaLM/hsT+FwAbgcMJRuQSz3Mlt3UVcAbBiOHVQ/QzqK6uLjZuKZa3UmR0tLa2bp8KS6RYxWIx5s+fTyyW8v/lBVfs8eVLuR2HYskA/gxMMLPPJ5TVhP/+Afi8mY0DMLO3mNnE5AaGsK2/LnAXcLKZ1YRtfAj4C0HitaeZ1Yanck/IZmcIRg+fdfc+4JPAUBef/QZoBI4i2NeM9fT08FrvYHmqSHmIRqOaCkuKXltbG2vXrqW9vb3QoaRU7PHlS7kdh6JI5tzdgZOB95rZk2Z2H9AGfIVg5OphYJWZPQT8H5mdHr4SeNDMrnX3VcBi4D6CGxeucvfV7r4N+EZYdjvwaJa79BOgycz+TnCKddCRPXd/HbgD+FW+724VEZHcicVidHR04O50dHQU3ahPsceXL+V4HIrmmjl3f5bgBoBUzgt/Ei0Pf/rrn53welbC668QJIX9y5cBl6Xo/3Jgh7tIE9sKly8cpJ8XCK+Zc/cngOkJ1f5fWD4gZoDwxod3AB9J7ltK38YtFbwejepBsTmgabCk2LW1tdHX1wdAb28v7e3tLFiwoMBRvaHY48uXcjwORTEyN1aFDzaOAn8KE0ARESlRy5YtIx6PAxCPx1m6dGmBIxqo2OPLl3I8DkUzMjcWufvDwJsLHYeMnr1q+nQ3a45odFOK3ezZs1myZAnxeJyqqirmzJlT6JAGKPb48qUcj4NG5kRERHKgqamJiorga7WyspK5c4tr5sdijy9fyvE4KJkrA9XV1Uyo3OHRcyJlRXOaSrGrra2lsbERM6OxsZHa2tpChzRAsceXL+V4HHSatQxMmTKFrfFnCx2GyKjSnKZSCpqamujs7Cza0Z5ijy9fyu04WPBUECklDQ0NvmLFiu3Lzc3NaU3nla43pvPSk1KytWFzJdMOOULXzImIyKDMbKW7Nwy/ZWoamSsDuT71NLGrC4DxU6bktN2xaBq5f39EREQSKZkrAzr9JCIiMnbpBggRERGREqZkTkRERKSEKZkTERERKWG6Zq4MtLa2Eo1GB5R1hTcxTEm6iSESiegaOxERkTKiZK4MRKNR1jz0CL01k7eXVW55BYDntlYllL2Y99hERERkdOk0a5norZlMz4HHbf/pramlt6Y2qWzy8A0JALFYjPnz5xOLxQodioiIyJCUzJWBrq4uKl57dVT7aG1tpbW1dVT7KCZtbW2sXbuW9vb2QociIiIyJCVzZaCnpwfr2zaqfUSj0R2uyytXsViMjo4O3J2Ojg6NzomISFHTNXNjSMVrrxKNbqK5uTnjutFolOrq6lGIqvi0tbXR19cHQG9vL+3t7SxYsKDAUYmIiKSmkTmRJMuWLSMejwMQj8dZunRpgSMSEREZnEbmxpC+CbsQefNeI5r0fSSjeaVq9uzZLFmyhHg8TlVVFXPmzCl0SCIiIoPSyJxIkqamJioqgl+NyspK5s6dW+CIREREBleSyZyZbS50DABmto+Z3Ri+nmFmxxUijurqarxi3Kj2EYlEiEQio9pHsaitraWxsREzo7Gxkdra2kKHJCIiMiidZs2Cuz8DnBouzgAagCXp1jezKnePZxvHlClTeG7rxmybGdJYmzWiqamJzs5OjcqJiEjRM3cvdAwZM7PN7j7JzGYBFwEbCZKpm4G1QDNQDZzs7v8ws8VAD3AgMBX4FNAEvBO4193PSGw3fH0qcIK7nxHWf5UgWfs34MvufqOZ1QO3AzOBaNhnF3AJ8CTww7CsB/iUuz9mZmcAxwMTgInh9je6+61hv9cCN7j7bYPtf0NDg69YsWL7cnNz844zQGz+F7hDZUK+3htn0sSaEY2wDTY9WDnRVGciIlIIZrbS3RtGWr8cRuYOBw4CXgT+CVzl7m8zs2ZgHnBOuN3uwL8DJwK/BY4GPg3cb2Yz3H3NMP3sDRxDkBDeBtzYv8LdXzezrwMN7n42gJntArzH3eNmNhu4GDglrPJOYLq7v2hm7wUWALea2a7AuwgSzQHM7CzgLIC6uroB61IlZ+vWvQC924jsvCVpzetsXb9ih+2H072pEoCt8WczrlsKNmyuLHQIIiIiI1IOydz97v4sgJn9A/hjWL4WODZhu9+6u5vZWmCju68N66wD6oE1w/Rzi7v3AQ+b2V5pxLUr0GZm0wAHEi9qW+ruLwK4+51m9mMz2xP4MHBTqlOv7n4lcCUEI3OJ61KNJjU3N7N1/QrOm5mbmSEuXrULQM7aKzb9+yciIlJqSvIGiCRbE173JSz3MTBZ3Zpim+TtEpOkCUP0Y2nE9U3gDnc/FPiPpPa6k7a9Bjid4PTvz9Noe4CxNtWWlK5i/6ymmpM3m3l6s6kbjUY5/vjjB8y8UkxzBucjlmLa33wotv0ttniS5fr3tZSVQzKXSxvN7CAzqwA+lGHdTcDOCcu7ElwPB3DGMHUXE54Odvd1GfY7pqbaktJW7J/VVHPyZjNPbzZ1W1pa6O7upqWlJSft5Vo+Yimm/c2HYtvfYosnWa5/X0uZkrmBvkpwQ8OfgUwvDrsDONjM1pjZx4DvAJeY2d3AkBdkuftG4BFGMConIrmRak7ebObpzaZuNBqls7MTgM7OTqLRaFHNGZyPWIppf/Oh2Pa32OJJluvf11JXktfM9d9x6u7LgeUJ5bMSXm9f13+3avi6Ezg0YTlx3Y0k3NiQapuk/re3FV4Dd1RS1bckvD4/3G4xwUjcdmZWA0wDrk/uOx1dXV309PQMmKUhGo2y0zbl6unauKWC16PRMTXTRSEU8xy/qebkdfcRz9ObzRy/iaNx/cvTp08vmjmD8zF/8VibI7nY9rfY4kmW69/XUqdv+wIL73R9FGh191cKHY/IWJVqTt5s5unNpm7/qFzicjHNGZyPWIppf/Oh2Pa32OJJluvf11JXkiNz5cTdlwF1w244hP5nvyXOudp/N6ukZ6+aPsZPjYxo3lpJXzGPfKaak9fdRzxPbzZz/NbX1w9I6Orr65k+fXrRzBmcj/mLx9ocycW2v8UWT7Jc/76WOo3MiYiQek7ebObpzabuwoULd1gupjmD8xFLMe1vPhTb/hZbPMly/fta6pTMlYGxNG+qlLZi/qymmpM3m3l6s6kbiUSor68HglG5SCRSVHMG5yOWYtrffCi2/S22eJLl+ve11Ok0axnQFFRSKor9s5pqTt5s5unNpu7ChQtpbm4eMEpXTHMG5yOWYtrffCi2/S22eJLl+ve1lJXk3KxjXfLcrKk0NzfzxLrV1E3qzUmf68PpvKbunJv2is2GzZVMO+QIXTMnIiJ5p7lZJaVcn8qa2BU8/3h8eLNFuZlG7o+ZiIhIPiiZK1PFfjpLREREckM3QIiIiIiUMCVzIiIiIiVMyZyIiIhICdM1c2Xg05/+NC+//DJTpkwhEonoejkREZExRMlcGXj22WfZ3L2F5196tdChiIiISJ7pNGsZ2Lp1K5jRWzN5e1lrayutra0FjEpERETyQSNzZaCvrw+SHv4cjUYLFI2IiIjkk0bmREREREqYRubKSMVrrxKNbqK5uZloNEp1dXWhQxIREZFRppE5ERERkRKmZK6M9E3YhUgkwqJFi4hEIkwp03lURURE5A1lm8yZ2eY0tnm3ma0zszVmltE5STM72cwOTlj+hpnNHkmsIiIiIiM11q+ZOx34nrv/fAR1TwZuBx4GcPev5zCujFRUVNDrfQPKIpFIgaIRERGRfCrbkbl+ZjbLzJab2Y1m9qiZXWuBTwMfBb5uZteG2/6Pmd1vZg+a2UUJbcwNyx4ws2vM7F3AicB3w1G9A8xssZmdGm7/PjNbbWZrzexqMxsflnea2R7h6wYzWx6+fm/Yzpqw3s6Z7OP48eOhonJA2bx58zQThIiIyBgwVkbmjgAOAZ4B7gaOdverzOwY4HZ3v9HM3g9MA94GGHCbmb0HiAFfC+u8YGaT3f1FM7utvy6AmRH+OwFYDLzP3R83s3bg88APh4jvXOCL7n63mU0CXst4D3vjVG55EdgLCB4anM6z5rq6ugCK5vo6TUcmIiKSmbGSzN3n7k8DmNkaoB74a9I27w9/VofLkwiSu8OBG939BQB3f3GYvt4KPOnuj4fLbcAXGTqZuxu4LBwhvLk/1kRmdhZwFkBdXd2AdXvvvfeAuVkheGjwE+tWUzepd8hguzcFI3pb488Os1ujb8PmyuE3EhERkQHGSjK3NeF1L6n324BL3P3/BhSazQc8xfaDsSHWxXnj1PaE/kJ3v9TMfgccB/zdzGa7+6OJFd39SuBKgIaGhgHxXHXVVSk7q5vUy3kzh56v9eJVuwAMu10+9MciIiIi6Sv7a+Yy8AfgzPA0J2Y2xcz2BP4EfNTMasPy/glQNwGprm17FKg3s/47ED4J3Bm+7gSODF+f0l/BzA5w97Xu/m1gBXBgJoFrHtbykc17GY1GOf7440c0lVssFmP+/PnEYrFRrZtNP/lor5yV4rFKFXMp7odkT+/70JTMhdz9j8B1wD1mtha4EdjZ3dcB3wLuNLMHgMvCKr8E/ie8YeGAhHZeAz4F/Dpspw+4Ilx9EbDIzP5CMELY7xwzeyhsvwf4fSaxR6NRzcVaJrJ5L1taWuju7qalpSXjum1tbaxdu5b29vZRrZtNP/lor5yV4rFKFXMp7odkT+/70Mo2mXP3SeG/y939hITys919cfj6jP4bGMLlRe5+WPjzTnf/R1je5u6Huvvh7n5GWHa3ux/s7ke4+z8S23L3P4Xlh7n7me6+NSz/i7u/xd3f7e7nuvussHxeQvsf799eJF3RaJTOzk4AOjs7M0oIY7EYHR0duDsdHR0Z/c83k7rZ9JOP9spZKR6rVDGX4n5I9vS+D2+sXDNX1rq6uujp6aG5uXl7WTQaZadtpZWrb9xSwevR6ID9GGtGOqdu8mhcS0sLixcvTqtuW1sbfX3Bcwp7e3tpb29nwYIFOa+bTT/5aK+cleKxShWzu5fcfkj2SvHzm2+l9W0vIin1j8oNtjyUZcuWEY/HAYjH4yxdunRU6mbTTz7aK2eleKxSxVyK+yHZ0/s+PI3MlYH+Z8QtWrRoe1lzczNb168oVEgjsldNH+OnRgbsx1gz0lHJ+vr6AQlcfX192nVnz57NkiVLiMfjVFVVMWfOnFGpm00/+WivnJXisUoVs7uX3H5I9krx85tvGpkTKQMLFy4ccnkoTU1NVFQEfwoqKyuZO3fuqNTNpp98tFfOSvFYpYq5FPdDsqf3fXhK5spAJBLRXKxlYqTvZSQS2T4aV19fn1EbtbW1NDY2YmY0NjZSW1s7KnWz6Scf7ZWzUjxWqWIuxf2Q7Ol9H55Os5aBwaa/2rC5ctgH8a4PZ4Aohgf2bthcybRCB1Fg2UxltnDhQpqbmzMalevX1NREZ2fniP7Hm0ndbPrJR3vlrBSPVaqYS3E/JHt634dm7plMbiDFoKGhwVesGPp6OM3NKiIiUhrMbKW7N4y0vkbmypQSIhERkbFB18yJiIiIlDAlcyIiIiIlTMmciIiISAlTMlcGWltbaW1tLXQYIiIiUgBK5spAR0cHHR0dhQ5DRERECkDJnIiIiEgJUzJXBrZs2cKWLVsKHUbZi8VizJ8/n1gsVuhQREREtlMyVwbcHT38efS1tbWxdu1a2tvbCx2KiIjIdkrmRNIQi8Xo6OjA3eno6NDonIiIFA0lcyJpaGtro6+vD4De3l6NzomISNFQMieShmXLlhGPxwGIx+MsXbq0wBGJiIgElMyJpGH27NlUVQVTGVdVVTFnzpwCRyQiIhJQMpeCmbmZXZOwXGVmz5vZ7Vm0eV6a23Wa2R4j7UdGR1NTExUVwa9LZWUlc+fOLXBEIiIiASVzqXUDh5pZdbg8B+jKss20krmRMDPMbLSaF6C2tpbGxkbMjMbGRmprawsdkoiICKBkbii/B44PX38cuL5/hZlNNLOrzex+M1ttZieF5WeY2c1m1mFmT5jZd8LyS4FqM1tjZteGZbeY2UozW2dmZ2UTaE1NDTU1Ndk0IWloamrisMMO06iciIgUlapCB1DEfgl8PTy1Oh24Gnh3uO5rwJ/d/Uwz2w24z8yWhetmAEcAW4HHzKzV3b9qZme7+4yE9s909xfD0b/7zewmd8/Z8y5aW1uJRqO5ai5tXV3BAOaUKVPy3nc+mBnf+MY3Ch3GmBWJRJg3b16hwxARKSpK5gbh7g+aWT3BqNySpNXvB040s3PD5QlAXfj6T+7+CoCZPQxMBZ5K0cV8M/tQ+Ho/YBowaDIXjt6dBVBXVzdgXWNj4w7bR6NRnli3mrpJvYM1OSq6N1UCsDX+bF77lfK3YXNloUMQESlKSuaGdhvwPWAWkHiRlAGnuPtjiRub2dsJRuT69ZLiGJvZLGA28E5332JmywkSwkG5+5XAlQANDQ0DpnsYbKSiblIv5818dahmc+7iVbsA5L1fKX/9ny0RkUKKxWJcdNFFXHDBBUVz/bSumRva1cA33H1tUvkfgHkW3nVgZkek0dY2MxsXvt4VeClM5A4E3pFNkK2trbS2tmbThIiISMnLx/dhMU7tqGRuCO7+tLsvSrHqm8A44EEzeyhcHs6V4fbXAh1AlZk9GNb9ezZxRqPRglwfJyIiUkxG+/uwWKd21GnWFNx9Uoqy5cDy8HUP8NkU2ywGFicsn5Dw+ivAVxI2/+AgfddnGm9XVxc9PT00NzdvL4tGo+y0Tbm6lI+NWyp4PRod8DkXEUkUjUaprq4efsMRSjW144IFC0atv3Tp215EREQkDcU6taNG5spA/2NAFi1644xwc3MzW9evKFRIIjm3V00f46dGBnzORUQSjfbI/ezZs1myZAnxeLyopnbUyJyIiIhIGop1akclc2UgEokQiUQKHYaIiEhBjfb3YbFO7ajTrGVAT8QXERHJz/dhU1MTnZ2dRTMqB0rmytqGzZV5f9Dq+nAGCD3gVXJtw+ZKphU6CBEZ82pra7n88ssLHcYASubKVKFOu04M52YdX6Zzs0rhTKNwn2sRkWKmZK5M6dSriIjI2KAbIERERERKmJI5ERERkRKmZE5ERESkhOmauTLQ2tq6w8TCXeGNCFPSuBEhEonoGjsREZESpWSuDESjUdY89Ai9NZO3l1VueQWA57YO/RZXbnlxVGMTERGR0aVkrkz01kym58Djti9XP7oEYEBZKv3biYiISGnSNXNloKuri4rXXi10GEBwyre1tXVAWSwWY/78+cRisYzbi0ajHH/88QNOI6cqy7VsYhYREcknJXNloKenB+vbVugwgCDRSk6y2traWLt2Le3t7Rm319LSQnd3Ny0tLUOW5Vo2MYuIiOSTkjkZVbFYjI6ODtydjo6OjEa6otEonZ2dAHR2dm5PFJPLiilmERGRfNM1c2NcxWuvEo1uorm5OSftRaNRqqurty+3tbXR19cHQG9vL+3t7SxYsCCttpJH3lKNxLW0tLB48eKRB5xCNjGLiIjkm0bmZFQtW7aMeDwOQDweZ+nSpWnX7R+BS1xOVZZr2cQsIiKSbxqZG+P6JuxC5M17sWjRopy0lzzCN3v2bJYsWUI8Hqeqqoo5c+ak3VZ9ff2AZK2+vh4gZVkuZROziIhIvpXtyJyZuZldk7BcZWbPm9ntw9SbYWZDP88j2O4MM/tRhjFtDv/dx8xuzKRuqWpqaqKiIviYVVZWMnfu3LTrLly4cIflVGW5lk3MIiIi+Va2yRzQDRxqZv0XcM0ButKoNwMYNpnLhrs/4+6n5qq96upqvGJcrprLSiQSIRKJbF+ura2lsbERM6OxsZHa2tqM2uofeauvr9/ednJZrmUTs4iISL6V+2nW3wPHAzcCHweuB94NYGYTgVbgMILjcGG4/TeAajM7BrgEeBL4IVAN9ACfcvfHwvb3M7MOYH/gOne/KGz7S8CZ4TZXufsPE4Mys3rgdnc/1MwqgW8DHwAc+Km7D3xQ2zCmTJnC8y89MuABwJVbgjswh3socDADxF6ZdDekVNOCNTU10dnZOaIRroULF9Lc3DxgBC5VWa5lE7OIiEg+mbsXOoZREZ7SfBfwdeATwN+Bc4Bz3f0EM7sYeNjdf2FmuwH3AUcAHwEa3P3ssJ1dgC3uHjez2cDn3f0UMzuDINk7FNgC3A+cQZCQLQbeARhwL/AJd19tZpvdfVJSMvd5YDbwsbCPye6+wxxbZnYWcBZAXV3dkevXr9++Lpu5WTOZw7UQij2+sU7z+oqIZM/MVrp7w0jrl/XInLs/GCZOHweSh6jeD5xoZueGyxOAuhTN7Aq0mdk0gkQt8XzmUnePAZjZzcAx4Ta/cffuhPJ3A6sHCXM2cIW7x8OYU06W6u5XAlcCNDQ0DMjAs/kybW5u5ol1q9kaf3bEbYym7k2VAEUb31i2YXNloUMQERHKPJkL3QZ8D5gFJF78ZMApCadMg0KztyfV/yZwh7t/KEwMlyesSx7W9LDdTFiKdjLSP33WSJO6ukm9nDezOKYDS3bxql0Aija+saz/vRGRzGXzdzsWi3HRRRdxwQUXbL+mNxqN0tzczKJFi0blWuKh+pbsxGIxampq3ppNG2nfAGFmNWZ2vpn9NFyeZmYnZNN5nlwNfMPd1yaV/wGYZ2YGYGZHhOWbgJ0TttuVN26cOCOpjTlmNjm8yeJk4G7gLuDk8HhNBD4E/GWI+P4IfM7MqsI4Jmewb0DqKbRERKR4ZfN3O9V0g/mY5nCwviU7bW1tVFRUTMqmjUzuZv05sBV4Z7j8NDC6n5occPen3T3VQ9S+SXDK9EEzeyhcBrgDONjM1pjZx4DvAJeY2d1A8nmlvwLXAGuAm9x9hbuvIrhm7j6C6+WucvfBTrECXAVsCON4APjPEeymiIiMAammG8zHNIeD9S3Z6T+m2crkNOsB7v4xM/s4gLv39I9qFSN33yHLdfflhKdJ3b0H+GyKbV4EjkoqfkvC6/PD7RYTJG2p+r4MuGywmNy9k+DGCcJr5b4U/oxIV1cXPT09I5qSKxqNstO2cn5CjYyWjVsqeD08tSMimUme+jBdqaYbfOCBBwZsMxrTHA7Wt6Y6zE7iMc1GJt/ir4enEx3AzA4gGKkTERGRPEg13WA+pjkcrG/JTuIxzUYmI3MXAB0Ez1a7FjiaHa8hkwLof2zHSKbkam5uZuv6FbkOScaAvWr6GD81krOp4ETGkpGOaKeabvCBBx4Y9WkOB+tbstN/TLOV9sicuy8FPkyQwF1P8Cy25VlHICIiImlJNd1gPqY5HKxvyU7iMc1Gpi1MIbgJYCfgPWb24awjkKwlT6ElIiLFbaR/t1NNN5iPaQ4H61uy039Ms5X2aVYzuxqYDqwD+q/Wc+DmrKOQrOgJ/CIipSWbv9upphvMxzSHg/Ut2WlqauL888/fnE0baU/nZWYPu/vB2XQmudHQ0OArVuTmOrf+GSDqJvXmpL1cWx/OADF15+KMbyzbsLmSaYccoWvmRESylM/pvO4xs4Pd/eGRdibFp9hPz04M52Ydr7lZi840iv/zIyIyFmSSzLURJHTPETySxAB39+mjEpnkhU7RioiIlLZMkrmrgU8Ca3njmjkRERERKaBMkrkN7n7bqEUiIiIiIhnLJJl71MyuA35LwswP7q67WUVEREQKJJNkrpogiXt/QpkeTVJCWltbAV0nJyIiUk7STubc/VOjGYiMvo6ODkDJnIiISDnJ5KHBE4D/Ag4BJvSXu/uZoxCXiIiIiKQhk+m8rgH+DfgAcCewL7BpNIKS7MViMebPn08sFttetmXLFrZs2TKiuiIiIlKcMknmIu5+PtDt7m3A8cBhoxOWZKutrY21a9fS3t6+vczdSWfGj1R1RUREpDhlksxtC/992cwOBXYF6nMekWQtFovR0dGBu9PR0ZHRCFs2dUVERCT/MknmrjSz3YHzgduAh4HvjEpUkpW2tjb6+oLnOvf29mY0wpZNXREREcm/tJM5d7/K3V9y9zvd/c3uvqe7XzGawcnILFu2jHg8DkA8Hmfp0qV5qSsiIiL5l3YyZ2bjzew/zew8M/t6/89oBicjM3v2bKqqghuVq6qqmDNnTl7qioiISP5lcpr1VuAkIA50J/wMyszczK5JWK4ys+fN7PaRBJspM+s0sz1Gs56ZzRpuf8xshpkdl4u20tHU1ERFRfDWVlZWMnfu3P72MbMR1RUREZHilEkyt6+7f8zdv+Pu3+//GaZON3ComVWHy3OArhFFWtpmAMMmc7lSW1tLY2MjZkZjYyO1tbUA1NTUUFNTM6K6IiIiUpwymc7rb2Z2mLuvzbCP3xM8xuRG4OPA9cC7AczsQmCzu38vXH4IOAF4HvgVwbPsKoFvuvsNZnYUsAiYSDC12PuAU4AGdz87bON24HvuvjwxCDP7EtD/gOOr3P2HZjYxVT8JdaqB3wA3AdcBrQSPY6kCLnT3W5P6mJi8Tbj/3wCqzewY4BLgSeCHBFOk9QCfcvfHMjqqw4jH49TU1PDEE0/Q3NwMQE9PD8D25URdXUGOPWXKFLZt27ZD3bEmEolopgwRESkJmSRzxwBnmNmTBImUAe7u04ep90vg62GSNR24mjCZG0Ij8Iy7Hw9gZrua2U7ADcDH3P1+M9uFIBEalpkdCXwKeHsY971mdifw5uR+EqpNCmNvd/d2M7sY+LO7n2lmuwH3mdmypK6+lrwNsAz4OgMTzl2A97h73MxmAxcTJKVD7cNZwFkAdXV1w+5zV1cXvnUTfc88wNawbNeqYCB26/oVO2zfvakyWBd/FoD9xkHfMy9vrzuWbNhcWegQRERE0pZJMvfBoVaa2e7u/lJyubs/aGb1BKNyS9Lsay3wPTP7NnC7u//FzA4DnnX3+8N2Xw37Tae9Y4DfuHt3WOdmgoSyI7mfhDq3At9x92vD5fcDJ5rZueHyBCA5q0pnGwie0ddmZtMAB8YNtwPufiVwJUBDQ8PwT/4F6ib1ct7MV9PZlItX7QKQ9vblrP9YiIiIlIJMHk2yPtVPwiZ/GqL6bcD3CE6xJoonxTAh7Otx4EiCpO6S8K5ZI0h8kqVsI0nKjG+QfvrdDXzQ3sgWDTjF3WeEP3Xu/kiKfobbBuCbwB3ufijwH4PEnLbW1lZaW1uzaUKkYAr1+S3UtHXlMl1euexHusba/kLx73O68eV6u2yMVh+Z3AAxnKGGyK4GvpHiertOYCaAmc0E9g9f7wNscfdfECSBM4FHgX3C6+Yws53NrCpsY4aZVZjZfsDbUvR/F3CymdWE17V9CPjLIP30+zoQA34SLv8BmNef3JnZESn6GWybTcDOCdvtyhs3gpyRop2MRKNRotFots2IFEShPr+FmrauXKbLK5f9SNdY218o/n1ON75cb5eN0eojl8ncoKf+3P1pd1+UYtVNwGQzWwN8Hng8LD+M4Jq0NQTXobW4++vAx4BWM3sAWEowonU3wQ0FawkSslUp+l8FLCa4hu1eghsgVqfqJ6nqOcAEM/sOwWjaOODB8EaNb6bYn8G2uQM42MzWmNnHCGbOuMTM7ia48UJE8qhQ09aVy3R55bIf6Rpr+wvFv8/pxpfr7fIR80hkcs1cxtx9Uoqy5cDy8HUPwXVmyToJRrmS694PvCPF9qcP0n99wuvLgMuS1v9hkH7qExY/lfD6sym2Xc7A/Um1zYvAUUnFb0l4fX5yW5no6uqip6dnwJ2n0WiUnbblMlcfOzZuqeD1aHTM3smbb9FolOrq6uE3zKFU09YtWLCgbPvNtXLZj3SNtf2F4t/ndOPL9Xb5iHkk8nWaVUSkaBRq2rpymS6vXPYjXWNtf6H49znd+HK9XT5iHom0R+bM7ADgaXffamazCB4z0u7uL4ebvC9nUUlGpkyZAsCiRW+cyW5ubk75CBIZ3l41fYyfGhlwPGX0FGIEdPbs2SxZsoR4PJ7XaesK1W+ulct+pGus7S8U/z6nG1+ut8tHzCORycjcTUCvmUWAnxHcrHBd/8rwVKKISNEr1LR15TJdXrnsR7rG2v5C8e9zuvHlert8xDwSmSRzfe4eJ7gT9IfuvgDYO2eRyIhFIhEikUihwxAZkUJ8fgs1bV25TJdXLvuRrrG2v1D8+5xufLneLh8xj0QmN0BsM7OPA00Ez0aDNB52K6NP005JKSvU57epqYnOzs68jzgUqt9cK5f9SNdY218o/n1ON75cb5eN0erD3NOaTAAzOxj4HHCPu19vZvsTTK11aU4jkmE1NDT4ihVDXw/X3NzME+tWUzepN60214fTeU3dOb3ty9mGzZVMO+QIXTMnIiJ5YWYr3b1hpPXTHplz94eB+WGnuwM7K5ErXpmetprYFTzDeHx4M8VYNo3Mj5+IiEihZHI363LgxLDOGuB5M7vT3b80OqFJNnTqVUREZGzI5AaIXcPJ7T8M/NzdjwRmj05YIiIiIpKOTJK5KjPbG/gocPsoxSMiIiIiGcgkmfsGwdRXUXe/38zeDDwxOmGJiIiISDoyuQHi18CvE5b/CZwyGkFJZlpbW4lGowPKusIbGt773vfq+jkREZEylskNEBOA/wIOASb0l7v7maMQl2QgGo2y5qFH6K2ZvL2scssr0BvfIckTERGR8pLJadZrgH8DPgDcCewLbBqNoCQzXV1deEUVPQcet/2nt6YWKofP1VtbW2ltbc1DlCNz6623MmvWLH77298WOhQREZGilEkyF3H384Fud28DjgcOG52wJBM9PT1Y37YR1Y1Go0U9evfDH/4QgMsuu6ywgYiIiBSpTJK5/mzhZTM7FNgVqM95RCKhW2+9lf4ZStxdo3MiIiIpZDI365XhzA/nA7cBk4Cvj0pUkht9vUSjUZqbmwfdJBqNUl1dnceg0tc/Ktfvsssu4z/+4z9SbywiIjJGZXI361XhyzuBN49OOCJvSJ43ON15hEVERMaSYZM5Mxtyui5318VMxaqikkgkMuSE8UON2hWamQ1I4MysgNGIiIgUp3Sumds5/JmU8DqxTGRUnHPOOQOWv/QlTQMsIiKSbNhkzt0vcveLgAOARQnLl1MEN0CYmZvZ9xOWzzWzC3PcR72ZPZTLNnOpuroarxg3orqRSIRIJJLjiHLjpJNO2j4aZ2a6Xk5ERCSFTO5mne7uL/cvuPtLwBE5jyhzW4EPm9kehQ6kUKZMmULfhF1GVHfevHlFPUNE/+icRuVERERSy+Ru1goz2z1M4jCzyRnWHy1x4EpgAfC1xBVm9ibgCqAuLDrH3e8OY7+a4EaOLcBZ7v5gOKJ3ADAF2A/4jrv/NKnNeoIHKE8Mi85297+Z2d7ADcAuBMfl8+7+FzPbDPwYmA28BJwHfCeM6Rx3v83MDgF+DuxEkGCf4u4ZzXtbueVFqh9d8sby5n+B9w17N2u6+qcHmzJlStZtZerwww9n2bJlLFu2LO99j3WRSKSok30REcksGfs+8DczuxFw4KPAt0Ylqsz9GHjQzL6TVL4I+IG7/9XM6oA/AAcBFwGr3f1kM/t3oB2YEdaZDryDIFlbbWa/S2rzX8Acd3/NzKYB1wMNwH8Cf3D3b5lZJVATbj8RWO7uXzGz3wAtwBzgYKCN4DEvnyM4hX2tme0EVCbvoJmdBZwFUFdXN2BdqtOk69a9AL297DfuZbauXzHYcUtb96YgpK3xZ7NuS0rDhs07fAxFRKQIZfJoknYzWwH8O2DAh9394VGLLAPu/qqZtQPzgZ6EVbOBgxPugtzFzHYGjgFOCev+2cxqzWzXcJtb3b0H6DGzO4C3AWsS2hwH/MjMZgC9wFvC8vuBq81sHHCLu/fXeR3oCF+vBba6+zYzW8sb1xzeA3zNzPYFbk41KufuVxKMQNLQ0DDgGR2pRk6am5vZun4F5818dYd1I3HxquA0bq7ak+LX/56LiEhxy+SaOdz9YXf/kbu3Fksil+CHwH/xxulPCPbvne4+I/yZ4u6bCJLRZJ70b3J5vwXARuBwghG5nQDc/S7gPUAXcI2ZzQ233+ZvPF+jj+AaP9y9jzCZdvfrgBMJEtE/hKOFaSv2+VVFRGSgdP9ux2Ix5s+fTywWy7iPbOrmSzQa5fjjjx/RtJK53r98HK9UfcRiMWpqat6aTbsZJXPFzN1fBH5FkND1+yNwdv9COJoGcBdwelg2C3jB3fuHnE4yswlmVgvMIhhxS7Qr8GyYjH2S8JSomU0F/hVeY/czYGa6sZvZm4F/uvvlBKddp6dbF4p/flURERko3b/bbW1trF27lvb29oz7yKZuvrS0tNDd3U1LS0vGdXO9f/k4Xqn6aGtro6KiIqtHvZVNMhf6PpB4V+t8oMHMHjSzhwmuTQO4sL8cuBRoSqhzH/A74O/AN939maQ+fgI0mdnfCU6xdofls4A1Zraa4BTu4E/q3dHHgIfMbA1wIME1fCIiMobFYjE6Ojpwdzo6OjIaMcqmbr5Eo1E6OzsB6OzszGhQItf7l4/jlaqP/rJsFcPdqFlx90kJrzfyxo0HuPsLBIlScp0XgZMGafJxdz8raftO4NDw9RMMHDn7f2F5G8ENDUPFd2Gqde5+CXDJIPEMq6uri56engF3rUajUXbaVm65uuTTxi0VvJ6ju6FFZKB05sVua2ujr68PgN7eXtrb21mwYEFa7WdTN1+SR+NaWlpYvHhxWnVzvX/5OF6p+nD37WXZ0Le9iIhIEVq2bBnxeByAeDzO0qVL81I3X/pH5QZbHkqu9y8fxytVH4ll2Sj5kblcSh45KxX9z35LnIO1/25WkZHaq6aP8VOHnttXREYmnRHv2bNns2TJEuLxOFVVVcyZMyft9rOpmy/19fUDErj6+vq06+Z6//JxvFL14e4sWbJk+MrD0MiciIhIEWpqaqKiIviarqysZO7cucPUyE3dfFm4cOGQy0PJ9f7l43il6iOxLBtK5spAMc+vKiIiO0rn73ZtbS2NjY2YGY2NjdTW1qbdfjZ18yUSiWwfjauvr8/oeyzX+5eP45Wqj/6ybOk0axkYbLqlDZsrc/bg1/XhDBB6kOzYsWFzJdMKHYRImUp3mrympiY6OztHNFKUTd18WbhwIc3NzRmNyvXL9f7l43il6qOpqYnzzz9/czbt2hvPs5VS0dDQ4CtWDH09XGtra06fPVfIuVmlcDQ3q4jI6DOzle7eMNL6GpkrU/oCFhERGRt0zZyIiIhICVMyJyIiIlLClMyJiIiIlDBdMyc5v1mi1Ax2c4cu/hcRkVKgZE6IRqOseegRemsmFzqUgqjc8goAz22tSih7sVDhiIiIZETJnADQWzOZngOPK3QYBVH9aDCVSuL+95eJiIgUO10zVwZaW1tpbW0tdBgS0vshIuUgFosxf/58YrFYoUORYSiZKwPRaHRMX/NWbPR+iEg5aGtrY+3atbS3txc6FBmGkjkREREZIBaL0dHRgbvT0dGh0bkip2vmykBXVxc9PT00NzePqH40GqXidU3rlqjitVeJRjeN6JhGo1Gqq6tHISoRkfxoa2ujr68PgN7eXtrb21mwYEGBo5LBaGROREREBli2bBnxeByAeDzO0qVLCxyRDEUjc2Wg//loixYtGlH95uZmVv5zYy5DKnl9E3Yh8ua9RnRMRzpCKiJSLGbPns2SJUuIx+NUVVUxZ86cQockQxiTI3Nm5mb2/YTlc83swgKGJCIiUjSampqoqAhShMrKSubOnVvgiGQoYzKZA7YCHzazPUZS2cyKakQzEokQiUQKHYaE9H6ISKmrra2lsbERM6OxsZHa2tpChyRDKKqkJI/iwJXAAuBriSvM7E3AFUBdWHSOu98djtztA9QDL5jZAcCZ7r4urLcc+G/gSeBq4M3AFuAsd38wrL/Z3b8Xbv8QcALwPPArYF+gEvimu9+Qyc5oyqniovdDRMpBU1MTnZ2dGpUrAWM1mQP4MfCgmX0nqXwR8AN3/6uZ1QF/AA4K1x0JHOPuPWa2APgocIGZ7Q3s4+4rzawVWO3uJ5vZvwPtwIwh4mgEnnH34wHMbNdc7WCyweZgjUajVHZvYdKqa3LXWV9v8G9FZe7aHC29wUW+A/a/Nz7iu1mLjeaYFZGRqK2t5fLLLy90GJKGMZvMufurZtYOzAd6ElbNBg42s/7lXcxs5/D1be7ev+2vgKXABQRJ3a/D8mOAU8I+/mxmtcMkaGuB75nZt4Hb3f0vqTYys7OAswDq6upSbTKsaDTKE+tWUzepd0D5fuOA3QBeH1G7qazfFCRxU3femrM2R8vGLcHVBnvVJO//62xdvyL/AeXQhs0lkEyLiEhWxmwyF/ohsAr4eUJZBfDOhKQNgDC56+5fdvcuM4uZ2XTgY8Bn+zdN0Y8TnNpNvEZxQtjO42Z2JHAccImZ/dHdv7FDA+5XEpwapqGhYcQPhaub1Mt5M18dafW0XbxqF4C89CWD638fRESkfI3VGyAAcPcXCUbY/iuh+I/A2f0LZjZjiCZ+CXwZ2NXd14ZldwGnh3VnAS+4+6tAJzAzLJ8J7B++3gfY4u6/AL7Xv00mNBeoiBSDUvxbpPlH86fYj3U+4otGoxx//PE5n/JxTCdzoe8DiXe1zgcazOxBM3sY+NwQdW8ETiNICPtd2F8fuBRoCstvAiab2Rrg88DjYflhwH1h+deAlkx3QHOBikgxKMW/RZp/NH+K/VjnI76Wlha6u7tpacn4q35IY/I0q7tPSni9EahJWH6B4LRpcp0LU5RtJOkYhqN9J6XYtgd4f4pwOglushARkTxKnn907ty5egTHKCn2Y52P+KLRKJ2dnQB0dnYSjUZz9hirMZnMlZt052aNRqPstE2DsWPJxi0VvB6NlsVduVL8Sm1eYs0/mj/FfqzzEV/yaFxLSwuLFy/OSdv6ZhcRkTFJ84/mT7Ef63zE1z8qN9hyNjQyVwbSnZu1ubm55B+1IZnZq6aP8VMjI563VyQTpTYCrPlH86fYj3U+4quvrx+QwNXX1+esbY3MlQFNHyUixaDU/hZp/tH8KfZjnY/4Fi5cOORyNpTMlYF58+bpCf8iUnCl9rdI84/mT7Ef63zEF4lEto/G1dfX5/Q/PjrNOsZs2FyZlwfJ9s8AoYfWFtaGzZVMK3QQIkVM84/mT7Ef63zEt3DhQpqbm3M6Kgdg7iOeTEAKpKGhwVesyPzat8HmZh0NXV1dwBvX80nhaG5WEZHiZmYr3b1hpPU1MjeG6AtdRESk/OiaOREREZESpmROREREpIQpmRMREREpYUrmJKdaW1tpbW0tdBgiIiJjhpI5yamOjg46OjoKHYaIiMiYoWROREREpITp0SSSU1u2bCl0CCIiImOKkjnJKT2EWkREJL90mlVERESkhCmZExERESlhSuZERERESpiSOREREZESVrLJnJn1mtkaM3vIzH5rZrvluP2/jaDOebmMQURERGQ4JZvMAT3uPsPdDwVeBL6Yy8bd/V3JZWZWOUy1jJI5C5Tye7ADM8PMCh2GiIjImFEuicQ9wBQAMzvAzDrMbKWZ/cXMDgzLPxKO4j1gZneFZWeY2a3h9o+Z2QX9DZrZ5vDfWWZ2h5ldB6wNy24J219nZmeFZZcC1eFo4bVh2ZfCPh8ys3PCsnoze8TMfgKsAs43sx8k9PsZM7tstA/YaKmpqaGmpqbQYYiIiIwZJf+cuXC07H3Az8KiK4HPufsTZvZ24CfAvwNfBz7g7l1Jp2TfBhwKbAHuN7PfufuKpG7eBhzq7k+Gy2e6+4tmVh3Wucndv2pmZ7v7jDCuI4FPAW8HDLjXzO4EXgLeCnzK3b9gZhOBB83sy+6+Lazz2WyPS2trK9FoNOW6rq4uAKZMmZJtNzuorq4GoLm5OedtDycSiTBv3ry89ysiIlJIpZzMVZvZGqAeWAksNbNJwLuAXyec6hsf/ns3sNjMfgXcnNDOUnePAZjZzcAxQHIyd19CIgcw38w+FL7eD5gGxJLqHAP8xt27E9p+N3AbsN7d/w7g7t1m9mfgBDN7BBjn7muTdzYcATwLoK6ubqjjAkA0GuWJdaupm9S7w7ruTcHZ4q3xZ4dtJ1N7hP9uXZ/7toeyYfNwZ8BFRETKUykncz3uPsPMdgVuJ7hmbjHwcv/oWCJ3/1w4Unc8sMbM+rdJnrIg1RQG3f0vzGwWMBt4p7tvMbPlwIQUdYa6cKw7afkqguvtHgV+nqqCu19JMOpIQ0NDWtMs1E3q5byZr+5QfvGqXQBSritV/fskUopisRgXXXQRF1xwAbW1tSXbh4gURslfM+furwDzgXOBHuBJM/sIbL/B4PDw9QHufq+7fx14gWBEDWCOmU0OT5meTDCCN5RdgZfCRO5A4B0J67aZ2bjw9V3AyWZWE55K/RDwl0H24d4wnv8Ers9g94HglGpra2um1USGpM9V/rS1tbF27Vra29tLug8RKYyST+YA3H018ABwGnA68F9m9gCwDjgp3Oy7ZrbWzB4iSLQeCMv/ClwDrAFuSnG9XLIOoMrMHgS+Cfw9Yd2VBNe/XevuqwhGCu8D7gWuCuMczK+Au939pTR2eYBoNDro9XEiI6XPVX7EYjE6Ojpwdzo6OojFkq/YKI0+RKRwSvY0q7tPSlr+j4TFxhTbfzi5LLyu7l/ufvZg7bv7cmB5QvlW4IODxPQV4CsJy5cBlyVt00lww0WyY4AfpCgfVldXFz09PQNuOohGo+y0rSxy9bRs3FLB69FoQW68KFfRaHT7DS0yetra2ujr6wOgt7eX9vZ2FixYUHJ9iEjhjJ1v+yJlZruZ2eME1wD+qdDxiEh+LVu2jHg8DkA8Hmfp0qUl2YeIFE7JjszlgrsvJjgVWsgYXgbekk0b/Y8YWbRo0fay5uZmtq4f7oxx+dirpo/xUyMDjoFkR6Oc+TF79myWLFlCPB6nqqqKOXPmlGQfIlI4GpkTESmgpqYmKiqCP8WVlZXMnTu3JPsQkcJRMlcGIpEIkUik0GFImdHnKj9qa2tpbGzEzGhsbByVx4bkow8RKZwxfZq1XGjWAxkN+lzlT1NTE52dnaM6YpaPPkSkMJTMlbENmytTPkx3fTgDRDk9aHfD5kqmFToIkRGqra3l8ssvL/k+RKQwlMyVqaFOj00M52YdPwpzsxbKNIbeZxERkXKlZK5M6RSZiIjI2KAbIERERERKmJI5ERERkRKmZE5ERESkhOmauTLR2trKnXfeCQQzQkQiEV03JyIiMgYomSsT0WiU51+IQWUVz7/0aqHDERERkTxRMldOKqvordGT3UVERMYSXTNXBlpbW+kKnx3Xr6uri9bW1gJFJCIiIvmiZK4MRKNRenp6BpT19PQQjUYLFJGIiIjki5I5ERERkRKma+bKQFdXVzAy58FyxWuv0tP72g6nXkVERKT8aGROREREpIQpmSsDU6ZMobq6GioqAeibsAvV1dVMmTKlwJGJiIjIaFMylyNm9jUzW2dmD5rZGjN7u5l1mtkeKbb9Wy77jkQiQTKXoLq6mkgkkstuREREpAjpmrkcMLN3AicAM919a5jA7TTY9u7+rlz2P2/evOChwS+9sr1sypQpmgFCRERkDNDIXG7sDbzg7lsB3P0Fd3+mf6WZVZtZh5l9JlzeHP47y8yWm9mNZvaomV1rZjbiKHrjVG6JUbnlxez2RkREREqGRuZy44/A183scWAZcIO73xmumwT8Emh39/YUdY8ADgGeAe4Gjgb+mryRmZ0FnAVQV1e3QyPd3d2Mq6oEnPHjd6Krq4vm5uaUwfbf5VrM19RpblkREZH0KJnLAXffbGZHAu8GjgVuMLOvhqtvBb7j7tcOUv0+d38awMzWAPWkSObc/UrgSoCGhgZPXj9x4kR24nXqJvUCPRB/ma3rn03ZYfem4EaJrfHU6wttw+bKQocgIiJSMpTM5Yi79wLLgeVmthZoClfdDXzQzK5z9x2SMGBrwuteRvCe9E/nVTepl/Nmvjrs9hev2gUgrW0LoT++UtA/ZZpGEQsjFotx0UUXccEFF1BbW9h5iaPRKM3NzSxatEg3H8mIFNPnWUqLrpnLATN7q5lNSyiaAawPX38diAE/Ga3+U03nJfkRjUY1bVoBtbW1sXbtWtrbU13BkF8tLS10d3fT0tJS6FCkRBXT51lKi5K53JgEtJnZw2b2IHAwcGHC+nOACWb2nQLEJlKWYrEYHR0duDsdHR3EYrGCxRKNRuns7ASgs7NTCb5krJg+z1J6dJo1B9x9JZDqcSP1Ca8/lbD9pPDf5QSnZvvLzx5J//3TeW3cVh65+cYtFbwenrIqdtFodIdn/El+tLW10dfXB0Bvby/t7e0sWLCgILEkj8a1tLSwePHigsQipamYPs9Sesrj219Expxly5YRj8cBiMfjLF26tGCx9I/KDbYsMpxi+jxL6dHIXBmYMmUKPT097DVu6/Abl4C9avoYPzXCokWLCh3KsEph9LBczZ49myVLlhCPx6mqqmLOnDkFi6W+vn5AAldfX1+wWKQ0FdPnWUqPRuZEpCQ1NTVRURH8CausrGTu3LkFi2XhwoVDLosMp5g+z1J6lMyVgVRzs0p+RCIRPYaiQGpra2lsbMTMaGxsLOijHCKRyPbRuPr6en0mJGPF9HmW0qPTrGWgf27WwR4SLKNHz5crrKamJjo7O4tiFGPhwoU0NzdrVE5GrJg+z1JaLPVzbKWYNTQ0+IoVKwaUNTc388S61eEMEENbH84AMXXn4bcthA2bK5l2yBElcc2ciIhItsxspbs3jLS+RubKRCandSaGc7OOL9K5WaeR2f6IiIiMZUrmyoRO94mIiIxNugFCREREpIQpmRMREREpYUrmREREREqYkjkRERGREqZkTkRERKSEKZkTERERKWFK5kRERERKmJI5ERERkRKmZE5ERESkhCmZExERESlhSuZERERESpiSOREREZESVlXoAMqVmfUCaxOKfunulxYqHhERESlPSuZGT4+7zxhJRTOrcvd4juMRERGRMqRkLs/M7OvAfwDVwN+Az7q7m9nycPlo4Dbg++m22draSjQapaurC4ApU6bkOuztIpEI8+bNG7X2RUREJDNK5kZPtZmtSVi+xN1vAH7k7t8AMLNrgBOA34bb7Obu703VmJmdBZwFUFdXN2BdNBrliXWrcQ+Wt8afzd1eJNiwuXJU2hUREZGRUzI3egY7zXqsmX0ZqAEmA+t4I5m7YbDG3P1K4EqAhoYGT15fN6l3++vzZr464qCHcvGqXUalXRERERk53c2aR2Y2AfgJcKq7Hwb8FJiQsEn3SNrt6upi45b8v5Wtra20trbmvd9iEovFmD9/PrFYrNCh5Fw575uISDlRMpdf/YnbC2Y2CTg1F4329PTwWq/loqmMRKNRotFo3vstJm1tbaxdu5b29vZCh5Jz5bxvIiLlRMnc6Kk2szUJP5e6+8sEo3FrgVuA+wsZoGQnFovR0dGBu9PR0VFWI1jlvG8iIuVG18yNEndPebeAuy8EFqYonzXaMWVr45YKXo9GaW5uBoKRuerq6gJHVThtbW309fUB0NvbS3t7OwsWLChwVLlRzvsmIlJuNDInMkLLli0jHg8eBxiPx1m6dGmBI8qdct43EZFyo2RO0rZXTR+RSIRFixaxaNEiIpHIqD7TrtjNnj2bqqpgcLuqqoo5c+YUOKLcKed9ExEpN0rmREaoqamJiorgV6iyspK5c+cWOKLcKed9ExEpN0rmykB1dTUTKnd49Nyoi0QiRCKRvPdbLGpra2lsbMTMaGxspLa2ttAh5Uw575uISLnRDRBlYMqUKaM268NQNK1XMILV2dlZliNX5bxvIiLlRMlcmdiwuXL7dF6jNVPDhs2VTBuVlktXbW0tl19+eaHDGBXlvG8iIuVEyVwZ6D/V2dXVBcD4UbopYVpCXyIiIlIclMyVAZ3uFBERGbt0A4SIiIhICVMyJyIiIlLClMyJiIiIlDAlcyIiIiIlTMmciIiISAlTMiciIiJSwpTMiYiIiJQwJXMiIiIiJUzJnIiIiEgJUzInIiIiUsKUzImIiIiUMM3NmmNm1gusTSj6pbtfWqh4REREpLwpmcu9HnefUeggREREZGzQadY8MLP3mdlvEpbnmNnN4ev3m9k9ZrbKzH5tZpMKF6mIiIiUGo3M5V61ma1JWL4E+BXwYzN7k7s/D3wK+LmZ7QEsBGa7e7eZfQX4EvCNbINobW0lGo1m28x2XV1dAEyZMiVnbRa7SCTCvHnzCh2GiIjIkJTM5V7K06xmdg3wCTP7OfBOYC7QCBwM3G1mADsB96Rq1MzOAs4CqKurGzaIaDTKE+tWUzepd2R7kaR7UyUAW+PP5qS9Yrdhc2WhQxAREUmLkrn8+TnwW+A14NfuHrcgg1vq7h8frrK7XwlcCdDQ0ODpdFg3qZfzZr6aRchvuHjVLgA5a6/Y9e+viIhIsdM1c3ni7s8AzxCcVl0cFv8dONrMIgBmVmNmb8m07dbWVlpbW3MVqsiYkI/fm1gsxvz584nFYkOWlbNi2t9iikUkl5TM5V61ma1J+El8LMm1wFPu/jBAeP3cGcD1ZvYgQXJ3YKYdRqPRnF4fJzIW5OP3pq2tjbVr19Le3j5kWTkrpv0tplhEcknJXI65e6W7z0j4+WrC6mOAnyZt/2d3P8rdp4c/t+U3YhEZDbFYjI6ODtydjo4OYrFYyrJyVkz7W0yxiOSarpnLEzNbCXQD/53rtru6uujp6aG5uXl7WTQaZadtytVHauOWCl6PRgccUykv0WiU6urqUWu/ra2Nvr4+AHp7e2lvb8fddyhbsGDBqMVQaKmOQaH2t5hiEck1fdvnibsf6e7vcfethY5FREbfsmXLiMfjAMTjcZYuXZqyrJwV0/4WUywiuaaRuTLQ/+y3RYsWbS9rbm5m6/oVhQqp5O1V08f4qZEBx1TKy2iPus6ePZslS5YQj8epqqpizpw5uPsOZeUs1TFQLCK5p5E5EZFR0NTUREVF8Ce2srKSuXPnpiwrZ8W0v8UUi0iuKZkrA5FIhEgkUugwRErKaP/e1NbW0tjYiJnR2NhIbW1tyrJyVkz7W0yxiOSaTrOWgcGmnNqwuTJnD79dH84AMVYeprthcyXTCh2EjKp8TNXW1NREZ2fngFGgVGXlrJj2t5hiEcklc09rMgEpIg0NDb5ixdDXw2lu1uxpblYREckHM1vp7g0jra+RuTKlJERERGRs0DVzIiIiIiVMyZyIiIhICVMyJyIiIlLClMyJiIiIlDAlcyIiIiIlTMmciIiISAnTo0lERERkgG3btvH000/z2muvFTqUsjJhwgT23Xdfxo0bl9N2lcyJiIjIAE8//TQ777wz9fX1mFmhwykL7k4sFuPpp59m//33z2nbOs0qIiIiA7z22mvU1tYqkcshM6O2tnZURjuVzImIiMgOlMjl3mgdUyVzIiIiUjCXX345Bx10EKeffnpG9To7O7nuuutGKarSomRORERECuYnP/kJS5Ys4dprr82o3kiTud7e3ozrFLsxlcyZWa+ZrTGzh8zs12ZWk0Hdfczsxiz6Pied/sxsuZk1jLQfERGRUvG5z32Of/7zn5x44ol861vf4swzz+Soo47iiCOO4NZbbwWCpO3d7343M2fOZObMmfztb38D4Ktf/Sp/+ctfmDFjBj/4wQ9YvHgxZ5999va2TzjhBJYvXw7ApEmT+PrXv87b3/527rnnHn7xi1/wtre9jRkzZvDZz3625BO8MZXMAT3uPsPdDwVeBz6XTiUzq3L3Z9z91Cz6PgdIO3kUEREpd1dccQX77LMPd9xxB93d3fz7v/87999/P3fccQf/8z//Q3d3N3vuuSdLly5l1apV3HDDDcyfPx+ASy+9lHe/+92sWbOGBQsWDNlPd3c3hx56KPfeey+1tbXccMMN3H333axZs4bKysqMRwWLzVh+NMlfgOlmNhFoBQ4jOB4XuvutZnYGcDwwAZhoZmcCt7v7oeG6EwmSswOA37j7lwHM7H+Bo4Bq4EZ3v8DM5gP7AHeY2Qvufmyq7fK25yIiIkXmj3/8I7fddhvf+973gOCO2g0bNrDPPvtw9tlnb0+8Hn/88Yzbrqys5JRTTgHgT3/6EytXruSoo44CoKenhz333DN3O1IAYzKZM7Mq4INAB/A14M/ufqaZ7QbcZ2bLwk3fCUx39xfNrD6pmRnAEcBW4DEza3X3p4CvhdtXAn8ys+nufrmZfQk41t1fCOun2u7BkexPa2sr0Wh0QFlXVxcAU6ZMGUmTKUUiEebNm5ez9kRERPq5OzfddBNvfetbB5RfeOGF7LXXXjzwwAP09fUxYcKElPWrqqro6+vbvpz4CJAJEyZQWVm5vZ+mpiYuueSSUdiLwhhryVy1ma0JX/8F+BnwN+BEMzs3LJ8A1IWvl7r7i4O09Sd3fwXAzB4GpgJPAR81s7MIju3ewMFAqiQt3e0I+zgLOAugrq5uwLpoNMoT61ZTN+mNc/7dm4IP7db4s4M1mZENmytz0o6IiEgqH/jAB2htbaW1tRUzY/Xq1RxxxBG88sor7LvvvlRUVNDW1rb9+radd96ZTZs2ba9fX1/PT37yE/r6+ujq6uK+++5L2c/73vc+TjrpJBYsWMCee+7Jiy++yKZNm5g6dWpe9nM0jLVkrsfdZyQWWPDQl1Pc/bGk8rcD3UO0tTXhdS9QZWb7A+cCR7n7S2a2mCA5HCDd7RK5+5XAlQANDQ2evL5uUi/nzXx1+/LFq3YBGFCWjf72ilUsFuOiiy7iggsuoLa2ttDhjHl6PyQX9DkaW84//3zOOeccpk+fjrtTX1/P7bffzhe+8AVOOeUUfv3rX3PssccyceJEAKZPn05VVRWHH344Z5xxBueccw77778/hx12GIceeigzZ85M2c/BBx9MS0sL73//++nr62PcuHH8+Mc/VjJX4v4AzDOzee7uZnaEu68eYVu7ECSAr5jZXgSncpeH6zYBOwMvDLNdxrq6uujdMrr3smzcUkFleOq2GLW1tbF27Vra29uHvRBWRp/eD8kFfY7Ghs7Ozu2v/+///m+H9dOmTePBB984cdV/enTcuHH86U9/GrDtYDcybN68ecDyxz72MT72sY+NNOSiM9buZk3lm8A44EEzeyhcHhF3fwBYDawDrgbuTlh9JfB7M7tjmO0y1tPTw2u9o/uk7td6jZ6enlHtY6RisRgdHR24Ox0dHcRisUKHNKbp/ZBc0OdIJH1jKplz90kpynrc/bPufpi7H+ruJ4Tli9397ITtOsNHmqRad4K7Lw9fn+HuB7n78e7+YXdfHJa3uvuB7n7sMNvNcvcVo3YQylBbW9v2i157e3tpb28vcERjm94PyQV9jkTSN6aSOSlPy5YtIx6PAxCPx1m6dGmBIxrb9H5ILuhzJJI+JXNS8mbPnk1VVXD5Z1VVFXPmzClwRGOb3g/JBX2ORNKnZE5KXlNTExUVwUe5srKSuXPnFjiisU3vh+SCPkci6VMyVwaqq6uZULnD00pyakKlU11dPap9jFRtbS2NjY2YGY2NjXqEQYHp/ZBc0OdIJH1K5srAlClT2Kumb/gNs7BXTV9OZ5PItaamJg477DD9771I6P2QXNDnaGzr7Ozk0EMP3aH805/+NA8//HDB4ygmes5cmdiwuXLAg33XhzNA5Ophvxs2VzItJy2NjtraWi6//PJChyEhvR+SC/ocFY+zv/Q//OuFwSZEytyee0zmR5d9d0R1r7rqqpzFMZz+2SZGKh6Pb7/2czQpmSsDkUhkh7KJ4QN+x+doNG3aIP2IiEj5+9cLL/KPvd6buwY33pnWZvF4nKamJlavXs1b3vIW2tvbOe644/je975HQ0MD119/PRdffDHuzvHHH8+3v/1tACZNmsQXv/hFli1bxu67787FF1/Ml7/8ZTZs2MAPf/hDTjzxRDo7O/nkJz9Jd3cw2dOPfvQj3vWud7F8+XIuuugi9t57b9asWcOSJUu2x/PPf/6TU045hSuvvJLJkyfzxS9+keeff56amhp++tOfcuCBB3LGGWcwefJkVq9ezcyZM/n+97+fu+M2CCVzZWDevHmFDkFERCTnHnvsMX72s59x9NFHc+aZZ/KTn/xk+7pnnnmGr3zlK6xcuZLdd9+d97///dxyyy2cfPLJdHd3M2vWLL797W/zoQ99iIULF7J06VIefvhhmpqaOPHEE9lzzz1ZunQpEyZM4IknnuDjH/84K1YEj3m97777eOihh9h///23z1Dx2GOPcdppp/Hzn/+cGTNm8L73vY8rrriCadOmce+99/KFL3yBP//5zwA8/vjjLFu2jMrK/MxrrmROREREitJ+++3H0UcfDcAnPvGJAafd77//fmbNmsWb3vQmAE4//XTuuusuTj75ZHbaaScaGxsBOOywwxg/fjzjxo3jsMMO256cbdu2jbPPPps1a9ZQWVnJ448/vr3tt73tbey///7bl59//nlOOukkbrrpJg455BA2b97M3/72Nz7ykY9s32br1jembP/IRz6St0QOlMyJiIhIkTKzQZfdB3+Kw7hx47ZvW1FRwfjx47e/7n8Y9Q9+8AP22msvHnjgAfr6+pgwYcL2+hMnThzQ3q677sp+++3H3XffzSGHHEJfXx+77bYba9asSdl/cv3RprtZRUREpCht2LCBe+65B4Drr7+eY445Zvu6t7/97dx555288MIL9Pb2cv311/Pe96Z/Xd8rr7zC3nvvTUVFBddcc82QNzvstNNO3HLLLbS3t3Pdddexyy67sP/++/PrX/8aCBLLBx54YIR7mT0lcyIiIlKUDjroINra2pg+fTovvvgin//857ev23vvvbnkkks49thjOfzww5k5cyYnnXRS2m1/4QtfoK2tjXe84x08/vjjw46mTZw4kdtvv50f/OAH3HrrrVx77bX87Gc/4/DDD+eQQw7h1ltvHfF+ZsuGGqaU4tTQ0OD9F2mKiIjk2iOPPMJBBx20fbmYHk1S6pKPLYCZrXT3hpG2qWvmREREZEhjNfEqFTrNKiIiIlLClMyJiIiIlDAlcyIiIiIlTMmciIiISAlTMiciIiJSwpTMiYiISNk57rjjePnll0dU95ZbbuHhhx/ObUCjqGweTWJmvcBagn16BGhy9y2FjUpERKT0nfffZ/PKCxtz1t6ue+zFxd//Uc7aS+TuuDtLliwZcRu33HILJ5xwAgcffHAOIxs9ZZPMAT3uPgPAzK4FPgdc1r/SzCrdffC5OkRERCSlV17YyFcOeDRn7X37H8Nv85WvfIWpU6fyhS98AYALL7wQM+Ouu+7ipZdeYtu2bbS0tHDSSSfR2dnJBz/4QY499ljuuecebrnlFt773veyYsUK9thjD04++WSeeuopXnvtNZqbmznrrLMAmDRpEs3Nzdx+++1UV1dz66238o9//IPbbruNO++8k5aWFm666SZ+97vfccUVV1BVVcXBBx/ML3/5y5wdi1wo19OsfwEiZjbLzO4ws+uAtWZWaWbfNbP7zexBM/ssgJlVmNlPzGydmd1uZkvM7NRwXaeZXWRmq8xsrZkdGJa/zcz+Zmarw3/fGpafYWY3m1mHmT1hZt/pD8rMGsN2HjCzP4X9PmFmb0qII2pme+T7gImIiBST0047jRtuuGH78q9+9Ss+9alP8Zvf/IZVq1Zxxx138N///d/0z2T12GOPMXfuXFavXs3UqVMHtHX11VezcuVKVqxYweWXX04sFgOgu7ubd7zjHTzwwAO85z3v4ac//Snvete7OPHEE/nud7/LmjVrOOCAA7j00ktZvXo1Dz74IFdccUX+DkKaymlkDgAzqwI+CHSERW8DDnX3J83sLOAVdz/KzMYDd5vZH4EjgXrgMGBPgtO0Vyc0+4K7zzSzLwDnAp8GHgXe4+5xM5sNXAycEm4/AzgC2Ao8ZmatwGvAT8M6T5rZZHfvM7NfAKcDPwRmAw+4+ws5PzCjoLW1lWg0mvd+u7q62G233bjqqqvy3reIiOTHEUccwb/+9S+eeeYZnn/+eXbffXf23ntvFixYwF133UVFRQVdXV1s3Bic/p06dSrveMc7UrZ1+eWX85vf/AaAp556iieeeILa2lp22mknTjjhBACOPPJIli5dmrL+9OnTOf300zn55JM5+eSTc7+zWSqnZK7azNaEr/8C/Ax4F3Cfuz8Zlr8fmN4/6gbsCkwDjgF+7e59wHNmdkdS2zeH/64EPpxQt83MpgEOjEvY/k/u/gqAmT0MTAV2B+7qj8Xd+ye5uxq4lSCZOxP4eaqdCxPRswDq6uqGOxZ5EY1GeWLdauom5ffs9YsvV9HT05PXPkVEJP9OPfVUbrzxRp577jlOO+00rr32Wp5//nlWrlzJuHHjqK+v57XXXgNg4sSJKdtYvnw5y5Yt45577qGmpoZZs2ZtrzNu3DjMDIDKykri8XjKNn73u99x1113cdttt/HNb36TdevWUVVVPClU8USSve3XzPUL36DuxCJgnrv/IWm744dpe2v4by9vHLNvAne4+4fMrB5YnmL7xDpGkPQN4O5PmdlGM/t34O0Eo3Q7cPcrgSsBGhoaBrTT2toKwLx584bZjdyrm9TLeTNfzWufZ94xma1btw6/oYiIlLTTTjuNz3zmM7zwwgvceeed/OpXv2LPPfdk3Lhx3HHHHaxfv37YNl555RV23313ampqePTRR/n73/8+bJ2dd96ZTZs2AdDX18dTTz3FscceyzHHHMN1113H5s2b2W233bLdvZwp12vmBvMH4PNmNg7AzN5iZhOBvwKnhNes7QXMSqOtXYGu8PUZaWx/D/BeM9s/7HtywrqrgF8AvxrJTRrRaLQgpzsLpc+DXy4RESlvhxxyCJs2bWLKlCnsvffenH766axYsYKGhgauvfZaDjzwwGHbaGxsJB6PM336dM4///xBT8UmOu200/jud7/LEUccwRNPPMEnPvEJDjvsMI444ggWLFhQVIkclNfIXDquIrg2bpUFw3bPAycDNwHvAx4CHgfuBV4Zpq3vEJxm/RLw5+E6dvfnw1OlN5tZBfAvYE64+jaC06spT7GKiIgU0q577JXWHaiZtJeutWvXbn+9xx57cM8996Tc7qGHHhqw3NnZuf3173//+5R1Nm/evP31qaeeyqmnBldhHX300QOeM/fXv/417XgLwfrvAhnrzGySu282s1rgPuBod38uT303AD9w93ens31DQ4OvWLFi+/Kpp55KT08PkUhktEJMKRqNstO2V1l0zMt57feMP0+Gikr+/Odhc2gRERmBRx55hIMOOqjQYZSlVMfWzFa6e8NI2xxrI3NDud3MdgN2Ar6Zx0Tuq8DnGeRaOREREZGhKJkLufusAvV7KXBpNm1MmTIFgEWLFuUipLQ1Nzezdf2K4TcUERGRUTPWboAQERGRNOgyrNwbrWOqkbkykO9r5QqtwoAK/T9ERGS0TJgwgVgsRm1t7fbnsEl23J1YLMaECRNy3raSuTJQiOfLFdK4Cqdi/PhChyEiUrb23Xdfnn76aZ5//vlCh1JWJkyYwL777pvzdpXMSVY2bK7k4lW75LXPrb1GdV57FBEZW8aNG8f+++9f6DAkTUrmZMQKdXp3clVX0T2wUUREpFCUzMmIjbXTuyIiIsVIV5GLiIiIlDDNAFGCzGwT8Fih45Dt9gBeKHQQMoDek+Ki96O46P0oPm91951HWlmnWUvTY9lM+yG5ZWYr9H4UF70nxUXvR3HR+1F8zCyrJ/DrNKuIiIhICVMyJyIiIlLClMyVpisLHYAMoPej+Og9KS56P4qL3o/ik9V7ohsgREREREqYRuZERERESpiSuRJiZo1m9piZRc3sq4WOZywys/3M7A4ze8TM1plZc1g+2cyWmtkT4b+7FzrWscTMKs1stZndHi7r/SgQM9vNzG40s0fD35N36v0oLDNbEP69esjMrjezCXpP8sfMrjazf5nZQwllgx5/M/t/4ff8Y2b2gXT6UDJXIsysEvgx8EHgYODjZnZwYaMak+LAf7v7QcA7gC+G78NXgT+5+zTgT+Gy5E8z8EjCst6PwlkEdLj7gcDhBO+L3o8CMbMpwHygwd0PBSqB09B7kk+LgcakspTHP/w+OQ04JKzzk/D7f0hK5krH24Cou//T3V8HfgmcVOCYxhx3f9bdV4WvNxF8UU0heC/aws3agJMLEuAYZGb7AscDVyUU6/0oADPbBXgP8DMAd3/d3V9G70ehVQHVZlYF1ADPoPckb9z9LuDFpOLBjv9JwC/dfau7PwlECb7/h6RkrnRMAZ5KWH46LJMCMbN64AjgXmAvd38WgoQP2LOAoY01PwS+DPQllOn9KIw3A88DPw9Pe19lZhPR+1Ew7t4FfA/YADwLvOLuf0TvSaENdvxH9F2vZK50WIoy3YpcIGY2CbgJOMfdXy10PGOVmZ0A/MvdVxY6FgGCEaCZwP+6+xFANzp9V1DhtVgnAfsD+wATzewThY1KhjCi73olc6XjaWC/hOV9CYbKJc/MbBxBInetu98cFm80s73D9XsD/ypUfGPM0cCJZtZJcOnBv5vZL9D7UShPA0+7+73h8o0EyZ3ej8KZDTzp7s+7+zbgZuBd6D0ptMGO/4i+65XMlY77gWlmtr+Z7URwgeRtBY5pzDEzI7ge6BF3vyxh1W1AU/i6Cbg137GNRe7+/9x9X3evJ/id+LO7fwK9HwXh7s8BT5nZW8Oi9wEPo/ejkDYA7zCzmvDv1/sIrvXVe1JYgx3/24DTzGy8me0PTAPuG64xPTS4hJjZcQTXB1UCV7v7twob0dhjZscAfwHW8sY1WucRXDf3K6CO4I/nR9w9+YJXGUVmNgs4191PMLNa9H4UhJnNILgZZSfgn8CnCAYO9H4UiJldBHyM4G781cCngUnoPckLM7semAXsAWwELgBuYZDjb2ZfA84keL/OcfffD9uHkjkRERGR0qXTrCIiIiIlTMmciIiISAlTMiciIiJSwpTMiYiIiJQwJXMiIiIiJUzJnIiIiEgJUzInIpIhMzvQzNaE848eMIL655hZzWjENkSfm8N/9zGzG/PZt4iMLj1nTkQkQ2b2VaDa3S8YYf1OoMHdX8igTpW7x0fSX1h/s7tPGml9ESleGpkTkbJgZvVm9oiZ/dTM1pnZH82s2syWm1lDuM0eYSKFmZ1hZreY2W/N7EkzO9vMvhSOtv3dzCYP0s9xwDnAp83sjrDsE2Z2Xzha939mVhmW/6+ZrQjjuSgsm08w4fkdCfU3J7R/qpktDl8vNrPLwu2+bWYHmFmHma00s7+Y2YFDHI/9zeweM7vfzL6ZdJweCl8fkhD3g2Y2LdP9CcsvNbOHwza+F5a9ycxuCvu/38yOTv/dFJFMKJkTkXIyDfixux8CvAycMsz2hwL/CbwN+Bawxd2PAO4B5qaq4O5LgCuAH7j7sWZ2EMFUSUe7+wygFzg93Pxr7t4ATAfea2bT3f1ygomzj3X3Y9PYp7cAs939v4ErgXnufiRwLvCTIeotAv7X3Y8Cnhtkm88Bi8K4G4CnM92fMOn9EHCIu08HWhL6/0HY/ykEU3yJyCioKnQAIiI59KS7rwlfrwTqh9n+DnffBGwys1eA34blawkSlnS8DzgSuD+Yx5xq4F/huo+a2VkEf2v3Bg4GHkyz3X6/dvdeM5sEvAv4ddgPwPgh6h3NG8nsNcC3U2xzD/A1M9sXuNndnzCzTPfnYeA14Coz+x1we7jt7P/fzv3D1hiFcRz//jpZ2iYixCRMWBiQWG1isLQhMbII8WcjrDaLwWIgDBabSESFxEAiBip0xCYm/yIiqT6G973pbelN27Sa23w/y73vybnnPOcON0/Oc84FtnfFOpRksP2+JS0hkzlJq8mvrve/aRKRSaarEGt69J/qep5i/r+PAW5W1fkZjclmmt2z3VX1uS2dzp6/o/vw8uw+P9rXAeBLu1s2Xz0PRVfV7STPgQPAgyTHWOB6qmoyyR6apPYwcBLY18a7t6p+LiBeSYtgmVXSaveBZqcJYGQZxn8EjCRZD5BkbZJNwBBNIvY1yQZgf9dnvgODXc+fkmxLMkBTsvxLVX0D3icZbedJkh094npKk1zBdJl0hiRbgHdt6fcuzW7kgtbT7hgOt+XnM8DOdvgxmsSuM1enXdISM5mTtNpdBo4neQasW+rBq2oCuAiMJXkNPAQ2VtU48BJ4C1ynSa46rgH3OxcggHM05cnHwMce0x0BjiYZb8c92KPvaeBEkhfA8Bx9DgFvkrwCtgK3FrGeQeBe2/cJcLZtPwXsai9FTNCcz5O0DPxrEkmSpD7mzpwkSVIf8wKEJM0hyVWaW6HdrlTVjZWI51+SXABGZzXfqapLKxGPpP/PMqskSVIfs8wqSZLUx0zmJEmS+pjJnCRJUh8zmZMkSepjJnOSJEl97A/V3wfrtk/mJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = pd.concat([icd_marker_2, disease_variant_icd_test])\n",
    "fig, ax = plot.subplots(figsize = (9, 9)) \n",
    "#ax.set_xscale(\"log\")\n",
    "plot.xlim(0, 100)\n",
    "ax = sns.boxplot(data=df_to_plot, x='num_feature_disease', y='class_name', hue='feature')\n",
    "ax.set(xlabel='num_feature_disease', ylabel='class_name')\n",
    "ax.set(title='Variant and gene distribution per ICD10 disease group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Pathway and gene distribution per ICD10 disease group')]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = pd.concat([icd_marker_2, disease_pathways_icd_test])\n",
    "fig, ax = plot.subplots(figsize = (9, 9)) \n",
    "plot.xlim(0, 150)\n",
    "ax = sns.boxplot(data=df_to_plot, x='num_feature_disease', y='class_name', hue='feature')\n",
    "ax.set(xlabel='num_feature_disease', ylabel='class_name')\n",
    "ax.set(title='Pathway and gene distribution per ICD10 disease group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-122-b00c48883f2e>:5: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  plot.xlim(0, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'log_num_feature_disease'), Text(0, 0.5, 'class_name')]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_to_plot = pd.concat([icd_marker_2, disease_gene_icd_test, disease_variant_icd_test, disease_pathways_icd_test])\n",
    "fig, ax = plot.subplots(figsize = (9, 13)) \n",
    "#plot.xlim(0, 150)\n",
    "ax.set_xscale(\"log\")\n",
    "plot.xlim(0, 200)\n",
    "ax = sns.boxplot(data=df_to_plot, x='num_feature_disease', y='class_name', hue='feature', showfliers = False)\n",
    "ax.set(xlabel='log_num_feature_disease', ylabel='class_name')\n",
    "#ax.set(title='Biomarkers, genes, variants and pathways distribution per ICD10 disease group')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}