Initial commit

README.md

+# REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data
+### REDIRECTION (dRug rEpurposing Disnet lInk pREdiCTION)
+##Content in each directory:
+
+- **/.**: Code files: 
+	- redirection (train & test) 
+	- heterograph_construction (build graph)
+	- plot_PRC (utility) 
+	- plot_ROC (utility) 
+	- testRepoDB (validate model using RepoDB)
+	- plot_Dist (plot distributions of RepoDB validation results).
+- **data**: Data to build DISNET's graph.
+- **documentation**: Instructions to install the needed libraries.
+- **metrics**: Training, testing and RepoDB validating ROC & PRC.
+- **models**: Trained model.
+- **results**: Result files of the RepoDB test and the distribution plots (once this are generated).
+- **testData**: Data to validate model using RepoDB.
+
+##Summary
+Repository of the CBMS2022 "REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data" paper.
+¿¿Maybe link??
\ No newline at end of file

data/Cora/Cora/raw/ind.cora.test.index

+2692
+2532
+2050
+1715
+2362
+2609
+2622
+1975
+2081
+1767
+2263
+1725
+2588
+2259
+2357
+1998
+2574
+2179
+2291
+2382
+1812
+1751
+2422
+1937
+2631
+2510
+2378
+2589
+2345
+1943
+1850
+2298
+1825
+2035
+2507
+2313
+1906
+1797
+2023
+2159
+2495
+1886
+2122
+2369
+2461
+1925
+2565
+1858
+2234
+2000
+1846
+2318
+1723
+2559
+2258
+1763
+1991
+1922
+2003
+2662
+2250
+2064
+2529
+1888
+2499
+2454
+2320
+2287
+2203
+2018
+2002
+2632
+2554
+2314
+2537
+1760
+2088
+2086
+2218
+2605
+1953
+2403
+1920
+2015
+2335
+2535
+1837
+2009
+1905
+2636
+1942
+2193
+2576
+2373
+1873
+2463
+2509
+1954
+2656
+2455
+2494
+2295
+2114
+2561
+2176
+2275
+2635
+2442
+2704
+2127
+2085
+2214
+2487
+1739
+2543
+1783
+2485
+2262
+2472
+2326
+1738
+2170
+2100
+2384
+2152
+2647
+2693
+2376
+1775
+1726
+2476
+2195
+1773
+1793
+2194
+2581
+1854
+2524
+1945
+1781
+1987
+2599
+1744
+2225
+2300
+1928
+2042
+2202
+1958
+1816
+1916
+2679
+2190
+1733
+2034
+2643
+2177
+1883
+1917
+1996
+2491
+2268
+2231
+2471
+1919
+1909
+2012
+2522
+1865
+2466
+2469
+2087
+2584
+2563
+1924
+2143
+1736
+1966
+2533
+2490
+2630
+1973
+2568
+1978
+2664
+2633
+2312
+2178
+1754
+2307
+2480
+1960
+1742
+1962
+2160
+2070
+2553
+2433
+1768
+2659
+2379
+2271
+1776
+2153
+1877
+2027
+2028
+2155
+2196
+2483
+2026
+2158
+2407
+1821
+2131
+2676
+2277
+2489
+2424
+1963
+1808
+1859
+2597
+2548
+2368
+1817
+2405
+2413
+2603
+2350
+2118
+2329
+1969
+2577
+2475
+2467
+2425
+1769
+2092
+2044
+2586
+2608
+1983
+2109
+2649
+1964
+2144
+1902
+2411
+2508
+2360
+1721
+2005
+2014
+2308
+2646
+1949
+1830
+2212
+2596
+1832
+1735
+1866
+2695
+1941
+2546
+2498
+2686
+2665
+1784
+2613
+1970
+2021
+2211
+2516
+2185
+2479
+2699
+2150
+1990
+2063
+2075
+1979
+2094
+1787
+2571
+2690
+1926
+2341
+2566
+1957
+1709
+1955
+2570
+2387
+1811
+2025
+2447
+2696
+2052
+2366
+1857
+2273
+2245
+2672
+2133
+2421
+1929
+2125
+2319
+2641
+2167
+2418
+1765
+1761
+1828
+2188
+1972
+1997
+2419
+2289
+2296
+2587
+2051
+2440
+2053
+2191
+1923
+2164
+1861
+2339
+2333
+2523
+2670
+2121
+1921
+1724
+2253
+2374
+1940
+2545
+2301
+2244
+2156
+1849
+2551
+2011
+2279
+2572
+1757
+2400
+2569
+2072
+2526
+2173
+2069
+2036
+1819
+1734
+1880
+2137
+2408
+2226
+2604
+1771
+2698
+2187
+2060
+1756
+2201
+2066
+2439
+1844
+1772
+2383
+2398
+1708
+1992
+1959
+1794
+2426
+2702
+2444
+1944
+1829
+2660
+2497
+2607
+2343
+1730
+2624
+1790
+1935
+1967
+2401
+2255
+2355
+2348
+1931
+2183
+2161
+2701
+1948
+2501
+2192
+2404
+2209
+2331
+1810
+2363
+2334
+1887
+2393
+2557
+1719
+1732
+1986
+2037
+2056
+1867
+2126
+1932
+2117
+1807
+1801
+1743
+2041
+1843
+2388
+2221
+1833
+2677
+1778
+2661
+2306
+2394
+2106
+2430
+2371
+2606
+2353
+2269
+2317
+2645
+2372
+2550
+2043
+1968
+2165
+2310
+1985
+2446
+1982
+2377
+2207
+1818
+1913
+1766
+1722
+1894
+2020
+1881
+2621
+2409
+2261
+2458
+2096
+1712
+2594
+2293
+2048
+2359
+1839
+2392
+2254
+1911
+2101
+2367
+1889
+1753
+2555
+2246
+2264
+2010
+2336
+2651
+2017
+2140
+1842
+2019
+1890
+2525
+2134
+2492
+2652
+2040
+2145
+2575
+2166
+1999
+2434
+1711
+2276
+2450
+2389
+2669
+2595
+1814
+2039
+2502
+1896
+2168
+2344
+2637
+2031
+1977
+2380
+1936
+2047
+2460
+2102
+1745
+2650
+2046
+2514
+1980
+2352
+2113
+1713
+2058
+2558
+1718
+1864
+1876
+2338
+1879
+1891
+2186
+2451
+2181
+2638
+2644
+2103
+2591
+2266
+2468
+1869
+2582
+2674
+2361
+2462
+1748
+2215
+2615
+2236
+2248
+2493
+2342
+2449
+2274
+1824
+1852
+1870
+2441
+2356
+1835
+2694
+2602
+2685
+1893
+2544
+2536
+1994
+1853
+1838
+1786
+1930
+2539
+1892
+2265
+2618
+2486
+2583
+2061
+1796
+1806
+2084
+1933
+2095
+2136
+2078
+1884
+2438
+2286
+2138
+1750
+2184
+1799
+2278
+2410
+2642
+2435
+1956
+2399
+1774
+2129
+1898
+1823
+1938
+2299
+1862
+2420
+2673
+1984
+2204
+1717
+2074
+2213
+2436
+2297
+2592
+2667
+2703
+2511
+1779
+1782
+2625
+2365
+2315
+2381
+1788
+1714
+2302
+1927
+2325
+2506
+2169
+2328
+2629
+2128
+2655
+2282
+2073
+2395
+2247
+2521
+2260
+1868
+1988
+2324
+2705
+2541
+1731
+2681
+2707
+2465
+1785
+2149
+2045
+2505
+2611
+2217
+2180
+1904
+2453
+2484
+1871
+2309
+2349
+2482
+2004
+1965
+2406
+2162
+1805
+2654
+2007
+1947
+1981
+2112
+2141
+1720
+1758
+2080
+2330
+2030
+2432
+2089
+2547
+1820
+1815
+2675
+1840
+2658
+2370
+2251
+1908
+2029
+2068
+2513
+2549
+2267
+2580
+2327
+2351
+2111
+2022
+2321
+2614
+2252
+2104
+1822
+2552
+2243
+1798
+2396
+2663
+2564
+2148
+2562
+2684
+2001
+2151
+2706
+2240
+2474
+2303
+2634
+2680
+2055
+2090
+2503
+2347
+2402
+2238
+1950
+2054
+2016
+1872
+2233
+1710
+2032
+2540
+2628
+1795
+2616
+1903
+2531
+2567
+1946
+1897
+2222
+2227
+2627
+1856
+2464
+2241
+2481
+2130
+2311
+2083
+2223
+2284
+2235
+2097
+1752
+2515
+2527
+2385
+2189
+2283
+2182
+2079
+2375
+2174
+2437
+1993
+2517
+2443
+2224
+2648
+2171
+2290
+2542
+2038
+1855
+1831
+1759
+1848
+2445
+1827
+2429
+2205
+2598
+2657
+1728
+2065
+1918
+2427
+2573
+2620
+2292
+1777
+2008
+1875
+2288
+2256
+2033
+2470
+2585
+2610
+2082
+2230
+1915
+1847
+2337
+2512
+2386
+2006
+2653
+2346
+1951
+2110
+2639
+2520
+1939
+2683
+2139
+2220
+1910
+2237
+1900
+1836
+2197
+1716
+1860
+2077
+2519
+2538
+2323
+1914
+1971
+1845
+2132
+1802
+1907
+2640
+2496
+2281
+2198
+2416
+2285
+1755
+2431
+2071
+2249
+2123
+1727
+2459
+2304
+2199
+1791
+1809
+1780
+2210
+2417
+1874
+1878
+2116
+1961
+1863
+2579
+2477
+2228
+2332
+2578
+2457
+2024
+1934
+2316
+1841
+1764
+1737
+2322
+2239
+2294
+1729
+2488
+1974
+2473
+2098
+2612
+1834
+2340
+2423
+2175
+2280
+2617
+2208
+2560
+1741
+2600
+2059
+1747
+2242
+2700
+2232
+2057
+2147
+2682
+1792
+1826
+2120
+1895
+2364
+2163
+1851
+2391
+2414
+2452
+1803
+1989
+2623
+2200
+2528
+2415
+1804
+2146
+2619
+2687
+1762
+2172
+2270
+2678
+2593
+2448
+1882
+2257
+2500
+1899
+2478
+2412
+2107
+1746
+2428
+2115
+1800
+1901
+2397
+2530
+1912
+2108
+2206
+2091
+1740
+2219
+1976
+2099
+2142
+2671
+2668
+2216
+2272
+2229
+2666
+2456
+2534
+2697
+2688
+2062
+2691
+2689
+2154
+2590
+2626
+2390
+1813
+2067
+1952
+2518
+2358
+1789
+2076
+2049
+2119
+2013
+2124
+2556
+2105
+2093
+1885
+2305
+2354
+2135
+2601
+1770
+1995
+2504
+1749
+2157

documentation/README.txt

+Development was carried out with Python 3.8.10
+To install all the libraries run the following command:
+pip install -r libsImport.txt
+
+Some libraries will produce an error, in that case delete the corresponding line and run the command again.
+
+
+IMPORTANT: PyTorch (torch) will be installed as a dependency of other packages, it should be uninstalled and installed again using the command shown below.
+IMPORTANT: Most libraries are compiled to use CUDA 11.3, if you are using a different version please adapt the installation.
+
+Here are some libraries that usually produce errors and the way to install them:
+	* DGL : pip install dgl-cu113==0.7.2 -f https://data.dgl.ai/wheels/repo.html
+	* SNAP : pip install snap-stanford
+	* PyTorch: pip install torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2 --extra-index-url https://download.pytorch.org/whl/cu113
+	* PyTorchExtra : pip install torch-scatter==2.0.9 torch-sparse==0.6.12 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==2.0.3 -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
+	
\ No newline at end of file

documentation/libsImport.txt

+asttokens==2.0.5
+attrs==21.4.0
+certifi==2021.10.8
+charset-normalizer==2.0.12
+click==8.1.2
+cycler==0.11.0
+deepsnap==0.2.1
+dgl-cu113==0.7.2
+executing==0.8.2
+Flask==2.1.1
+fonttools==4.29.1
+googledrivedownloader==0.4
+idna==3.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+install==1.3.5
+isodate==0.6.1
+itsdangerous==2.1.2
+Jinja2==3.0.3
+joblib==1.1.0
+kiwisolver==1.3.2
+littleutils==0.2.2
+MarkupSafe==2.1.0
+matplotlib==3.5.1
+networkx==2.6.3
+numpy==1.22.2
+nvidia-smi==0.1.3
+packaging==21.3
+pandas==1.4.1
+Pillow==9.0.1
+pluggy==1.0.0
+py==1.11.0
+pyparsing==3.0.7
+pytest==7.0.1
+python-dateutil==2.8.2
+pytz==2021.3
+PyYAML==6.0
+rdflib==6.1.1
+requests==2.27.1
+scikit-learn==1.0.2
+scipy==1.8.0
+seaborn==0.11.2
+six==1.16.0
+sklearn==0.0
+snap-stanford==6.0.0
+sorcery==0.2.2
+threadpoolctl==3.1.0
+tomli==2.0.1
+torch==1.10.2+cu113
+torch-cluster==1.5.9
+torch-geometric==2.0.3
+torch-scatter==2.0.9
+torch-sparse==0.6.12
+torch-spline-conv==1.2.1
+torchaudio==0.10.2+cu113
+torchvision==0.11.3+cu113
+tqdm==4.62.3
+typing-extensions==4.1.1
+urllib3==1.26.8
+Werkzeug==2.1.1
+wrapt==1.13.3
+yacs==0.1.8
+zipp==3.8.0

plot_Dist.py

+import numpy as np
+import pandas as pd
+
+
+def plot_dist(extension=''):
+    preds = [pd.read_csv("results/dis_dru_the_5013_table.csv"),pd.read_csv("results/dis_dru_the_5013R_table.csv")]
+    df = pd.DataFrame(data={'RepoDB':preds[0]['pred'],'Random':preds[1]['pred']})
+
+    ax = df.plot.hist(bins=50,alpha=0.5)
+    ax.set_xticks(np.arange(0,1.1,0.1))
+    ax.set_title("RepoDB & Random Prediction Histogram")
+    ax.set_xlabel("Prediction Score")
+    ax.figure.savefig("results/plots/histogram.svg", format = 'svg', dpi=1200)
+    ax.figure.clf()
+    
+    bx = df.plot.kde()
+    bx.set_title("RepoDB & Random Prediction")
+    bx.set_xlabel("Prediction Score")
+    bx.figure.savefig("results/plots/distribution"+extension+".svg", format = 'svg', dpi=1200)
+    bx.set_xlim([0, 1])
+    bx.figure.savefig("results/plots/distribution01"+extension+".svg", format = 'svg', dpi=1200)
+    bx.figure.clf()
+    
+plot_dist()
+

plot_PRC.py

+import sklearn.metrics as metrics
+import matplotlib.pyplot as plt
+
+
+def plot_prc(y_true, y_pred, edge, extension=''):
+    precision, recall, trhesholds = metrics.precision_recall_curve(y_true, y_pred)
+    average_precision = metrics.average_precision_score(y_true, y_pred)
+
+    plt.title('Precision-Recall Curve')
+    
+    plt.plot(recall, precision,label = " ".join(edge)+ " " +'RPC = %0.2f' % average_precision)
+    
+    plt.legend(loc = 'lower right')
+
+    plt.xlim([0, 1])
+    plt.ylim([0, 1])
+    plt.ylabel('Precision')
+    plt.xlabel('Recall')
+    plt.show()
+    plt.savefig('metrics/prc'+extension+'.svg', format = 'svg', dpi=1200)
+    plt.clf()
+    
+    print("PRC:", average_precision)
+    return recall, precision, average_precision