{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "from scipy import stats\n",
    "from statsmodels.stats.diagnostic import lilliefors\n",
    "from scipy.stats import mannwhitneyu, levene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DISNET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_gen = pd.read_csv('dis_genes.tsv', sep='\\t')\n",
    "dis_gen = dis_gen.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "disnet_score = dis_gen[\"score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "disnet_score = pd.DataFrame(disnet_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13747265487982685"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_gen[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    353628.000000\n",
       "mean          0.137473\n",
       "std           0.129536\n",
       "min           0.010000\n",
       "25%           0.050000\n",
       "50%           0.100000\n",
       "75%           0.130000\n",
       "max           1.000000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_gen[\"score\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REPODB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_repodb_target = pd.read_csv(\"score_gdas_repodb_target_final.tsv\", sep='\\t')\n",
    "cases_repodb_target = cases_repodb_target.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>drug_id</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>score</th>\n",
       "      <th>disease_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0007134</td>\n",
       "      <td>CHEMBL1908360</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.50</td>\n",
       "      <td>C0003873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0007134</td>\n",
       "      <td>CHEMBL1908360</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.50</td>\n",
       "      <td>C0004153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0007134</td>\n",
       "      <td>CHEMBL1908360</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.50</td>\n",
       "      <td>C0006413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0007134</td>\n",
       "      <td>CHEMBL1908360</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.50</td>\n",
       "      <td>C0007131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0007134</td>\n",
       "      <td>CHEMBL1908360</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.50</td>\n",
       "      <td>C0007137</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>7769</th>\n",
       "      <td>C0025202</td>\n",
       "      <td>CHEMBL1131</td>\n",
       "      <td>5915</td>\n",
       "      <td>0.02</td>\n",
       "      <td>C0033860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7770</th>\n",
       "      <td>C0025202</td>\n",
       "      <td>CHEMBL1131</td>\n",
       "      <td>6256</td>\n",
       "      <td>0.02</td>\n",
       "      <td>C0033860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7771</th>\n",
       "      <td>C0010674</td>\n",
       "      <td>CHEMBL1520</td>\n",
       "      <td>8654</td>\n",
       "      <td>0.03</td>\n",
       "      <td>C0242350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7772</th>\n",
       "      <td>C0026769</td>\n",
       "      <td>CHEMBL1201563</td>\n",
       "      <td>3454</td>\n",
       "      <td>0.03</td>\n",
       "      <td>C0751967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7773</th>\n",
       "      <td>C0026769</td>\n",
       "      <td>CHEMBL1201563</td>\n",
       "      <td>3455</td>\n",
       "      <td>0.02</td>\n",
       "      <td>C0751967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7774 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     disease_id        drug_id  gene_id  score disease_new\n",
       "0      C0007134  CHEMBL1908360     2475   0.50    C0003873\n",
       "1      C0007134  CHEMBL1908360     2475   0.50    C0004153\n",
       "2      C0007134  CHEMBL1908360     2475   0.50    C0006413\n",
       "3      C0007134  CHEMBL1908360     2475   0.50    C0007131\n",
       "4      C0007134  CHEMBL1908360     2475   0.50    C0007137\n",
       "...         ...            ...      ...    ...         ...\n",
       "7769   C0025202     CHEMBL1131     5915   0.02    C0033860\n",
       "7770   C0025202     CHEMBL1131     6256   0.02    C0033860\n",
       "7771   C0010674     CHEMBL1520     8654   0.03    C0242350\n",
       "7772   C0026769  CHEMBL1201563     3454   0.03    C0751967\n",
       "7773   C0026769  CHEMBL1201563     3455   0.02    C0751967\n",
       "\n",
       "[7774 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_repodb_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21210059171595477"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_repodb_target[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7774.000000\n",
       "mean        0.212101\n",
       "std         0.173242\n",
       "min         0.020000\n",
       "25%         0.050000\n",
       "50%         0.220000\n",
       "75%         0.340000\n",
       "max         1.000000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_repodb_target[\"score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_gdas_two_repodb = pd.read_csv('score_gdas_two_repodb.tsv', sep='\\t')\n",
    "score_gdas_two_repodb = score_gdas_two_repodb.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_gdas_one_repodb = pd.read_csv('score_gdas_one_repodb.tsv', sep='\\t')\n",
    "score_gdas_one_repodb = score_gdas_one_repodb.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_gdas_repodb = pd.concat([score_gdas_two_repodb,score_gdas_one_repodb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1623303834808263"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_gdas_repodb[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSBJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_csbj_target = pd.read_csv('score_gdas_csbj_target_filtergen.tsv', sep='\\t')\n",
    "cases_csbj_target = cases_csbj_target.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_csbj_target = cases_csbj_target.drop([\"Original Condition\",\"New Condition\",\"Drugs\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_csbj_target = cases_csbj_target.rename(columns={\"New Condition CUI\": \"disease_new\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28196850393700795"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_csbj_target[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_gdas_csbj = pd.read_csv('score_gdas_csbj.tsv', sep='\\t')\n",
    "score_gdas_csbj = score_gdas_csbj.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_gdas_csbj[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All data  DREBIOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gdas = pd.concat([score_gdas_csbj,score_gdas_repodb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16361764705882384"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gdas[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    340.000000\n",
       "mean       0.163618\n",
       "std        0.165744\n",
       "min        0.020000\n",
       "25%        0.030000\n",
       "50%        0.100000\n",
       "75%        0.300000\n",
       "max        0.700000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gdas[\"score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathways_score = all_gdas[\"score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathways_score = pd.DataFrame(pathways_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gdas_target_ = pd.concat([cases_csbj_target,cases_repodb_target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gdas_target_ = all_gdas_target_.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DREGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gdas_target_.to_csv(\"Triples_target_final.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_score = all_gdas_target_[\"score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_score = pd.DataFrame(target_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21280592063287349"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gdas_target_[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7837.000000\n",
       "mean        0.212806\n",
       "std         0.173944\n",
       "min         0.020000\n",
       "25%         0.050000\n",
       "50%         0.220000\n",
       "75%         0.340000\n",
       "max         1.000000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gdas_target_[\"score\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normality Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21647426452840834, 0.0009999999999998899)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(all_gdas_target_[\"score\"], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.745e+03, 1.500e+02, 1.329e+03, 2.175e+03, 1.280e+02, 2.290e+02,\n",
       "        4.600e+01, 2.300e+01, 1.100e+01, 1.000e+00]),\n",
       " array([0.02 , 0.118, 0.216, 0.314, 0.412, 0.51 , 0.608, 0.706, 0.804,\n",
       "        0.902, 1.   ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 71,
     "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": [
    "plt.hist(all_gdas_target_[\"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2729778045817372, 0.0009999999999998899)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(all_gdas[\"score\"], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([154.,  65.,  20.,   2.,  39.,  45.,   1.,   4.,   5.,   5.]),\n",
       " array([0.02 , 0.088, 0.156, 0.224, 0.292, 0.36 , 0.428, 0.496, 0.564,\n",
       "        0.632, 0.7  ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 73,
     "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": [
    "plt.hist(all_gdas[\"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.34968164864294105, 0.0009999999999998899)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " lilliefors(dis_gen[\"score\"], dist ='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([260222.,  13748.,  52148.,  17653.,   3423.,   2842.,   1791.,\n",
       "          1066.,    327.,    408.]),\n",
       " array([0.01 , 0.109, 0.208, 0.307, 0.406, 0.505, 0.604, 0.703, 0.802,\n",
       "        0.901, 1.   ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 75,
     "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": [
    "plt.hist(dis_gen[\"score\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MannWhitney-U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=1127106.5, pvalue=6.441560639405658e-07)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(all_gdas[\"score\"], all_gdas_target_[\"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=59656820.5, pvalue=0.3988562703545171)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(all_gdas[\"score\"],dis_gen[\"score\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "disnet_score[\"Drug_repositioning_type\"] = \"DISNET\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathways_score[\"Drug_repositioning_type\"] = \"DREBIOP\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_score[\"Drug_repositioning_type\"] = \"DREGE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_gdas_all = pd.concat([disnet_score,pathways_score,target_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>score</th>\n",
       "      <th>Drug_repositioning_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>DISNET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>DISNET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.10</td>\n",
       "      <td>DISNET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.10</td>\n",
       "      <td>DISNET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.10</td>\n",
       "      <td>DISNET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7769</th>\n",
       "      <td>0.02</td>\n",
       "      <td>DREGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7770</th>\n",
       "      <td>0.02</td>\n",
       "      <td>DREGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7771</th>\n",
       "      <td>0.03</td>\n",
       "      <td>DREGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7772</th>\n",
       "      <td>0.03</td>\n",
       "      <td>DREGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7773</th>\n",
       "      <td>0.02</td>\n",
       "      <td>DREGE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>361805 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      score Drug_repositioning_type\n",
       "0      0.10                  DISNET\n",
       "1      0.10                  DISNET\n",
       "2      0.10                  DISNET\n",
       "3      0.10                  DISNET\n",
       "4      0.10                  DISNET\n",
       "...     ...                     ...\n",
       "7769   0.02                   DREGE\n",
       "7770   0.02                   DREGE\n",
       "7771   0.03                   DREGE\n",
       "7772   0.03                   DREGE\n",
       "7773   0.02                   DREGE\n",
       "\n",
       "[361805 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_gdas_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_colors = [\"#4ea8af\",\"#d76b73\", \"#2ecc71\"] \n",
    "sns.set_palette( my_colors ) \n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x=\"Drug_repositioning_type\", y=\"score\", data=type_gdas_all, \n",
    "            width=0.7, order=[\"DREBIOP\", \"DREGE\", \"DISNET\"],\n",
    "            showfliers = False)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Value GDAs\")\n",
    "sns.despine()\n",
    "plt.savefig(\"plot_GDAS.svg\")\n",
    "plt.show()"
   ]
  }
 ],
 "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
}