result

8a6a37f8 · Belen Otero Carrasco · 3f736ca9 · 8a6a37f8 · 8a6a37f8 · 8a6a37f8
Commit 8a6a37f8 authored Dec 20, 2022 by Belen Otero Carrasco
28 changed files
--- a/Data/Input/DISNET/ATC_desc_name.xlsx
+++ b/Data/Input/DISNET/ATC_desc_name.xlsx
--- a/Data/Input/DISNET/Drug_Categories.xlsx
+++ b/Data/Input/DISNET/Drug_Categories.xlsx
--- a/Data/Input/DISNET/Triples_target_final.tsv
+++ b/Data/Input/DISNET/Triples_target_final.tsv
--- a/Data/Input/DISNET/Type_dis_drebiop.xlsx
+++ b/Data/Input/DISNET/Type_dis_drebiop.xlsx
--- a/Data/Input/DISNET/all_triplets_repodb_union.tsv
+++ b/Data/Input/DISNET/all_triplets_repodb_union.tsv
+	disease_PwB	drug_id	pathway_id	gene_id	disease_no_PwB
+0	C0020538	CHEMBL578	WP554	1636	C0018802
+1	C0020538	CHEMBL578	WP4756	1636	C0018802
+2	C0020538	CHEMBL577	WP554	1636	C0018802
+3	C0020538	CHEMBL577	WP4756	1636	C0018802
+4	C0020538	CHEMBL1513	WP554	185	C0004238
+5	C0020538	CHEMBL1513	WP554	185	C0011881
+6	C0020538	CHEMBL1513	WP4756	185	C0004238
+7	C0020538	CHEMBL1513	WP4756	185	C0011881
+8	C0030567	CHEMBL59	WP2371	6531	C0001206
+9	C0030567	CHEMBL59	WP2371	6531	C0020649
+10	C0030567	CHEMBL59	WP2371	6531	C0024586
+11	C0020538	CHEMBL191	WP554	185	C0010674
+12	C0020538	CHEMBL191	WP554	185	C0011881
+13	C0020538	CHEMBL191	WP4756	185	C0010674
+14	C0020538	CHEMBL191	WP4756	185	C0011881
+15	C0020538	CHEMBL1168	WP554	1636	C0004238
+16	C0020538	CHEMBL1168	WP4756	1636	C0004238
+17	C0020538	CHEMBL1393	WP554	1585	C0003962
+18	C0020538	CHEMBL1393	WP554	1585	C0013604
+19	C0020538	CHEMBL1393	WP554	1585	C0020428
+20	C0020538	CHEMBL1393	WP554	4306	C0003962
+21	C0020538	CHEMBL1393	WP554	4306	C0013604
+22	C0020538	CHEMBL1393	WP554	4306	C0020428
+23	C0020538	CHEMBL1017	WP554	185	C0020473
+24	C0020538	CHEMBL1017	WP4756	185	C0020473
+25	C0020538	CHEMBL1069	WP554	185	C0018801
+26	C0020538	CHEMBL1069	WP4756	185	C0018801
+27	C0035579	CHEMBL1042	WP1531	7421	C0004096
+28	C0035579	CHEMBL1042	WP1531	7421	C0009324
+29	C0035579	CHEMBL1042	WP1531	7421	C0010346
+30	C0035579	CHEMBL1042	WP1531	7421	C0011849
+31	C0035579	CHEMBL1042	WP1531	7421	C0020598
+32	C0035579	CHEMBL1042	WP1531	7421	C0020626
+33	C0035579	CHEMBL1042	WP1531	7421	C0024141
+34	C0035579	CHEMBL1042	WP1531	7421	C0026769
+35	C0035579	CHEMBL1042	WP1531	7421	C0028754
+36	C0035579	CHEMBL1042	WP1531	7421	C0029456
+37	C0035579	CHEMBL1042	WP1531	7421	C0029458
+38	C0030567	CHEMBL1201203	WP2371	6531	C0015371
+39	C0035579	CHEMBL1040	WP1531	7421	C0020598
+40	C0035579	CHEMBL1040	WP1531	7421	C0035086
+41	C0020538	CHEMBL1560	WP554	1636	C0011881
+42	C0020538	CHEMBL1560	WP4756	1636	C0011881
+43	C0030567	CHEMBL502	WP2355	4790	C0002395
+44	C0030567	CHEMBL502	WP2355	4790	C0026769
+45	C0035579	CHEMBL1536	WP1531	7421	C0020598
+46	C0035579	CHEMBL1536	WP1531	7421	C0020626
+47	C0035579	CHEMBL1536	WP1531	7421	C0029456
+48	C0035579	CHEMBL1536	WP1531	7421	C0029458
+49	C0035579	CHEMBL2356023	WP1531	7421	C0020598
+50	C0035579	CHEMBL2356023	WP1531	7421	C0020626
+51	C0035579	CHEMBL846	WP1531	7421	C0020598
+52	C0035579	CHEMBL846	WP1531	7421	C0020626
+53	C0035579	CHEMBL846	WP1531	7421	C0035086
+54	C0026769	CHEMBL25	WP673	4609	C0003862
+55	C0026769	CHEMBL25	WP673	4609	C0003873
+56	C0026769	CHEMBL25	WP673	4609	C0004153
+57	C0026769	CHEMBL25	WP673	4609	C0004604
+58	C0026769	CHEMBL25	WP673	4609	C0009443
+59	C0026769	CHEMBL25	WP673	4609	C0026764
+60	C0026769	CHEMBL25	WP673	595	C0003862
+61	C0026769	CHEMBL25	WP673	595	C0003873
+62	C0026769	CHEMBL25	WP673	595	C0004153
+63	C0026769	CHEMBL25	WP673	595	C0004604
+64	C0026769	CHEMBL25	WP673	595	C0009443
+65	C0026769	CHEMBL25	WP673	595	C0026764
+66	C0026769	CHEMBL25	WP673	7157	C0003862
+67	C0026769	CHEMBL25	WP673	7157	C0003873
+68	C0026769	CHEMBL25	WP673	7157	C0004153
+69	C0026769	CHEMBL25	WP673	7157	C0004604
+70	C0026769	CHEMBL25	WP673	7157	C0009443
+71	C0026769	CHEMBL25	WP673	7157	C0026764
+72	C0026769	CHEMBL1434	WP673	5601	C0001144
+73	C0026769	CHEMBL1434	WP673	5601	C0001261
+74	C0026769	CHEMBL1434	WP673	5601	C0003175
+75	C0026769	CHEMBL1434	WP673	5601	C0006277
+76	C0026769	CHEMBL1434	WP673	5601	C0006309
+77	C0026769	CHEMBL1434	WP673	5601	C0010674
+78	C0026769	CHEMBL1434	WP673	5601	C0011581
+79	C0026769	CHEMBL1434	WP673	5601	C0018081
+80	C0026769	CHEMBL1434	WP673	5601	C0023860
+81	C0030567	CHEMBL1373	WP2371	6531	C0027404
+82	C0030567	CHEMBL1373	WP2371	6531	C0030193
+83	C0030567	CHEMBL1175	WP2371	6531	C0016053
+0	C0030567	CHEMBL59	WP2371	6531	C0184567
+1	C0030567	CHEMBL59	WP2371	6531	C0242422
+2	C0030567	CHEMBL59	WP2371	6531	C0600177
+3	C0030567	CHEMBL59	WP2371	6531	C1621958
+4	C0020538	CHEMBL1237	WP554	1636	C0027051
+5	C0020538	CHEMBL1237	WP554	5972	C0027051
+6	C0020538	CHEMBL1237	WP4756	1636	C0027051
+7	C0020538	CHEMBL1237	WP4756	5972	C0027051
+8	C0020538	CHEMBL1581	WP554	1636	C0028754
+9	C0020538	CHEMBL1581	WP4756	1636	C0028754
+10	C0020538	CHEMBL1168	WP554	1636	C0038454
+11	C0020538	CHEMBL1168	WP4756	1636	C0038454
+12	C0020538	CHEMBL1017	WP554	185	C0038454
+13	C0020538	CHEMBL1017	WP4756	185	C0038454
+14	C0035579	CHEMBL1042	WP1531	7421	C0036337
+15	C0035579	CHEMBL1042	WP1531	7421	C0036341
+16	C0035579	CHEMBL1042	WP1531	7421	C0042870
+17	C0030567	CHEMBL1201203	WP2371	6531	C0242422
+18	C0020538	CHEMBL611	WP554	7040	C1739363
+19	C0002892	CHEMBL2110563	WP1533	4548	C0042847
+20	C0002892	CHEMBL2110563	WP1533	4548	C0162316
+21	C0002892	CHEMBL2103737	WP1533	4548	C0006114
+22	C0002892	CHEMBL2103737	WP1533	4548	C0033860
+23	C0002892	CHEMBL2103737	WP1533	4548	C0042847
+24	C0035579	CHEMBL1040	WP1531	7421	C0085682
+25	C0030567	CHEMBL1201236	WP2371	1644	C0184567
+26	C0030567	CHEMBL1201236	WP2371	1644	C0242422
+27	C0002395	CHEMBL502	WP2355	4790	C0026769
+28	C0002395	CHEMBL502	WP2355	4790	C0030567
+29	C0002395	CHEMBL502	WP2355	4790	C0036341
+30	C0002395	CHEMBL502	WP2355	4790	C0497327
+31	C0030567	CHEMBL502	WP2355	4790	C0036341
+32	C0030567	CHEMBL502	WP2355	4790	C0497327
+33	C0035579	CHEMBL1536	WP1531	7421	C0042870
+34	C0035579	CHEMBL1536	WP1531	7421	C3536984
+35	C0035579	CHEMBL2356023	WP1531	7421	C0039621
+36	C0035579	CHEMBL2356023	WP1531	7421	C0085682
+37	C0035579	CHEMBL846	WP1531	7421	C0042870
+38	C0035579	CHEMBL846	WP1531	7421	C0085682
+39	C0035579	CHEMBL846	WP1531	7421	C1527383
+40	C0026769	CHEMBL25	WP673	4609	C0027051
+41	C0026769	CHEMBL25	WP673	4609	C0029408
+42	C0026769	CHEMBL25	WP673	4609	C0030193
+43	C0026769	CHEMBL25	WP673	4609	C0032463
+44	C0026769	CHEMBL25	WP673	4609	C0040460
+45	C0026769	CHEMBL25	WP673	4609	C0149931
+46	C0026769	CHEMBL25	WP673	4609	C0393735
+47	C0026769	CHEMBL25	WP673	4609	C0948089
+48	C0026769	CHEMBL25	WP673	595	C0027051
+49	C0026769	CHEMBL25	WP673	595	C0029408
+50	C0026769	CHEMBL25	WP673	595	C0030193
+51	C0026769	CHEMBL25	WP673	595	C0032463
+52	C0026769	CHEMBL25	WP673	595	C0040460
+53	C0026769	CHEMBL25	WP673	595	C0149931
+54	C0026769	CHEMBL25	WP673	595	C0393735
+55	C0026769	CHEMBL25	WP673	595	C0948089
+56	C0026769	CHEMBL25	WP673	7157	C0027051
+57	C0026769	CHEMBL25	WP673	7157	C0029408
+58	C0026769	CHEMBL25	WP673	7157	C0030193
+59	C0026769	CHEMBL25	WP673	7157	C0032463
+60	C0026769	CHEMBL25	WP673	7157	C0040460
+61	C0026769	CHEMBL25	WP673	7157	C0149931
+62	C0026769	CHEMBL25	WP673	7157	C0393735
+63	C0026769	CHEMBL25	WP673	7157	C0948089
+64	C0026769	CHEMBL1434	WP673	5601	C0031099
+65	C0026769	CHEMBL1434	WP673	5601	C0031350
+66	C0026769	CHEMBL1434	WP673	5601	C0032064
+67	C0026769	CHEMBL1434	WP673	5601	C0032285
+68	C0026769	CHEMBL1434	WP673	5601	C0034362
+69	C0026769	CHEMBL1434	WP673	5601	C0035854
+70	C0026769	CHEMBL1434	WP673	5601	C0037199
+71	C0026769	CHEMBL1434	WP673	5601	C0039128
+72	C0026769	CHEMBL1434	WP673	5601	C0042029
+73	C0025202	CHEMBL1229517	WP4685	673	C0026764
+74	C0030567	CHEMBL1373	WP2371	6531	C0036341
+75	C0030567	CHEMBL1373	WP2371	6531	C1269683
+76	C0030567	CHEMBL1175	WP2371	6531	C0036341
+77	C0030567	CHEMBL1175	WP2371	6531	C0497327
+78	C0030567	CHEMBL1175	WP2371	6531	C1269683
--- a/Data/Input/DISNET/drug_atc.tsv
+++ b/Data/Input/DISNET/drug_atc.tsv
+	drug_id	ATC_code_id	ATC_LEVEL
+0	CHEMBL370805	N01BC01	N
+1	CHEMBL370805	R02AD03	R
+2	CHEMBL370805	S01HA01	S
+3	CHEMBL370805	S02DA02	S
+4	CHEMBL578	C09AA02	C
+5	CHEMBL64	J04AC01	J
+6	CHEMBL64	J04AC51	J
+7	CHEMBL86	A03FA01	A
+8	CHEMBL1123	A03AA07	A
+9	CHEMBL1139	B01AC09	B
+10	CHEMBL1466	B01AA01	B
+11	CHEMBL185	L01BC02	L
+12	CHEMBL185	L01BC52	L
+13	CHEMBL34259	L01BA01	L
+14	CHEMBL34259	L04AX03	L
+15	CHEMBL411	G03CB02	G
+16	CHEMBL411	G03CC05	G
+17	CHEMBL411	L02AA01	L
+18	CHEMBL469	M01AB15	M
+19	CHEMBL469	S01BC05	S
+20	CHEMBL485	N02AA59	N
+21	CHEMBL485	N02AA79	N
+22	CHEMBL485	R05DA04	R
+23	CHEMBL553025	L01CA04	L
+24	CHEMBL607	N02AB02	N
+25	CHEMBL607	N02AB52	N
+26	CHEMBL607	N02AB72	N
+27	CHEMBL679	A01AD01	A
+28	CHEMBL679	B02BC09	B
+29	CHEMBL679	C01CA24	C
+30	CHEMBL679	R01AA14	R
+31	CHEMBL679	R03AA01	R
+32	CHEMBL679	R03AK01	R
+33	CHEMBL679	S01EA01	S
+34	CHEMBL679	S01EA51	S
+35	CHEMBL80	A06AH04	A
+36	CHEMBL80	V03AB15	V
+37	CHEMBL15770	M01AB02	M
+38	CHEMBL83	L02BA01	L
+39	CHEMBL100116	N02AD01	N
+40	CHEMBL101	M01AA01	M
+41	CHEMBL101	M02AA01	M
+42	CHEMBL1024	L01AA06	L
+43	CHEMBL1029	A16AX06	A
+44	CHEMBL103	G03DA04	G
+45	CHEMBL1043	D10AX05	D
+46	CHEMBL1043	J04BA02	J
+47	CHEMBL107	M04AC01	M
+48	CHEMBL1082	J01CA04	J
+49	CHEMBL109	N03AG01	N
+50	CHEMBL112	N02BE01	N
+51	CHEMBL112	N02BE51	N
+52	CHEMBL112	N02BE71	N
+53	CHEMBL115	J05AE02	J
+54	CHEMBL1177	N06BA05	N
+55	CHEMBL1200455	V08AB02	V
+56	CHEMBL1200539	A02AC01	A
+57	CHEMBL1200539	A12AA04	A
+58	CHEMBL1200624	G03DC06	G
+59	CHEMBL1200679	B05XA12	B
+60	CHEMBL1200826	N05AN01	N
+61	CHEMBL1200939	B05XA04	B
+62	CHEMBL1200939	G04BA01	G
+63	CHEMBL1201129	L01BC08	L
+64	CHEMBL1201201	N06BA03	N
+65	CHEMBL1201220	V08AA01	V
+66	CHEMBL1201222	N06BA12	N
+67	CHEMBL1201291	V08AB03	V
+68	CHEMBL1201314	J05AB14	J
+69	CHEMBL1201356	G02AB01	G
+70	CHEMBL1201513	B01AB01	B
+71	CHEMBL1201513	B01AB51	B
+72	CHEMBL1201513	C05BA03	C
+73	CHEMBL1201513	C05BA53	C
+74	CHEMBL1201513	S01XA14	S
+75	CHEMBL1237044	N02AX02	N
+76	CHEMBL1242	G04BX06	G
+77	CHEMBL1282	D06BB10	D
+78	CHEMBL129	J05AF01	J
+79	CHEMBL1292	J04BA01	J
+80	CHEMBL1296	J01DC07	J
+81	CHEMBL1311	C01DA14	C
+82	CHEMBL1328	V04CG04	V
+83	CHEMBL1336	L01XE05	L
+84	CHEMBL1351	L01XA02	L
+85	CHEMBL135400	N05CF01	N
+86	CHEMBL137	A01AB17	A
+87	CHEMBL137	D06BX01	D
+88	CHEMBL137	G01AF01	G
+89	CHEMBL137	J01XD01	J
+90	CHEMBL137	P01AB01	P
+91	CHEMBL137	P01AB51	P
+92	CHEMBL1389	G03AC03	G
+93	CHEMBL1389	G03AD01	G
+94	CHEMBL139	D11AX18	D
+95	CHEMBL139	M01AB05	M
+96	CHEMBL139	M01AB55	M
+97	CHEMBL139	M02AA15	M
+98	CHEMBL139	S01BC03	S
+99	CHEMBL1397	J02AC04	J
+100	CHEMBL1401	P01AX11	P
+101	CHEMBL141	J05AF05	J
+102	CHEMBL1421	L01XE06	L
+103	CHEMBL1431	A10BA02	A
+104	CHEMBL1433	A01AB22	A
+105	CHEMBL1433	J01AA02	J
+106	CHEMBL1434	A01AB23	A
+107	CHEMBL1434	J01AA08	J
+108	CHEMBL1447	J01FF02	J
+109	CHEMBL1460	J05AF02	J
+110	CHEMBL1464	B01AA03	B
+111	CHEMBL1491	C08CA01	C
+112	CHEMBL1502	A02BC02	A
+113	CHEMBL1503	A02BC01	A
+114	CHEMBL1518	H03BA02	H
+115	CHEMBL1542	L04AX01	L
+116	CHEMBL159	L01CA01	L
+117	CHEMBL1591	D06AA01	D
+118	CHEMBL1591	J01AA01	J
+119	CHEMBL1595	N02AA08	N
+120	CHEMBL1595	N02AA58	N
+121	CHEMBL16	N03AB02	N
+122	CHEMBL16	N03AB52	N
+123	CHEMBL161	J01DD04	J
+124	CHEMBL161	J01DD54	J
+125	CHEMBL163	J05AE03	J
+126	CHEMBL1643	J05AP01	J
+127	CHEMBL1680	H01CB02	H
+128	CHEMBL170	M09AA72	M
+129	CHEMBL170	P01BC01	P
+130	CHEMBL1729	A03FA02	A
+131	CHEMBL174	J01CA01	J
+132	CHEMBL174	J01CA51	J
+133	CHEMBL174	S01AA19	S
+134	CHEMBL1741	J01FA09	J
+135	CHEMBL1751	C01AA05	C
+136	CHEMBL1757	J01XX01	J
+137	CHEMBL1790041	A02BA02	A
+140	CHEMBL19019	N07BB04	N
+141	CHEMBL1908315	G02AD03	G
+142	CHEMBL191	C09CA01	C
+143	CHEMBL192	G04BE03	G
+144	CHEMBL193	C08CA05	C
+145	CHEMBL193	C08CA55	C
+146	CHEMBL193240	R03DX07	R
+147	CHEMBL19490	M01AB04	M
+148	CHEMBL20	S01EC01	S
+149	CHEMBL2096647	V03AE03	V
+150	CHEMBL2159122	A07DA06	A
+151	CHEMBL23	C05AE03	C
+152	CHEMBL23	C08DB01	C
+153	CHEMBL2355051	G03GB02	G
+154	CHEMBL2362016	L01XX27	L
+155	CHEMBL25	A01AD05	A
+156	CHEMBL25	B01AC06	B
+157	CHEMBL25	N02BA01	N
+158	CHEMBL25	N02BA51	N
+159	CHEMBL25	N02BA71	N
+160	CHEMBL253371	A03AX08	A
+161	CHEMBL253371	A03AX58	A
+162	CHEMBL270190	A06AH02	A
+163	CHEMBL278020	N07AA01	N
+164	CHEMBL278020	N07AA51	N
+165	CHEMBL278020	S01EB06	S
+166	CHEMBL284906	C01DX16	C
+167	CHEMBL29	J01CE01	J
+168	CHEMBL29	S01AA14	S
+169	CHEMBL294199	M02AB01	M
+170	CHEMBL294199	N01BX04	N
+171	CHEMBL296306	A06AD11	A
+172	CHEMBL296306	A06AD61	A
+173	CHEMBL3	N07BA01	N
+174	CHEMBL30	A02BA01	A
+175	CHEMBL30	A02BA51	A
+176	CHEMBL325041	L01XX32	L
+177	CHEMBL3301668	H01BB03	H
+180	CHEMBL35	C03CA01	C
+181	CHEMBL3707210	D10AX04	D
+186	CHEMBL374731	J05AF11	J
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+194	CHEMBL384467	D10AA03	D
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+196	CHEMBL384467	R01AD03	R
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+201	CHEMBL384467	S03BA01	S
+202	CHEMBL398707	N02AA03	N
+203	CHEMBL404108	M01AH06	M
+204	CHEMBL405	N06BA01	N
+205	CHEMBL416	D05AD02	D
+206	CHEMBL416	D05BA02	D
+207	CHEMBL42	N05AH02	N
+208	CHEMBL428647	L01CD01	L
+209	CHEMBL44884	J04AK02	J
+210	CHEMBL456	C03CC01	C
+211	CHEMBL459	C02AB01	C
+212	CHEMBL465	A04AD10	A
+213	CHEMBL476	L01AX04	L
+214	CHEMBL480	A02BC03	A
+215	CHEMBL480	A02BC53	A
+219	CHEMBL485253	N02CA04	N
+220	CHEMBL490	N06AB05	N
+221	CHEMBL499	C07AA06	C
+222	CHEMBL499	S01ED01	S
+223	CHEMBL499	S01ED51	S
+224	CHEMBL508102	H03BB01	H
+225	CHEMBL521	C01EB16	C
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+113713	CHEMBL492	N01BB07	N
+113714	CHEMBL492	N01BB57	N
+115885	CHEMBL119443	G02AB03	G
+116124	CHEMBL2103827	C01CA27	C
+119250	CHEMBL1583	J01CA06	J
+120089	CHEMBL328560	N03AX03	N
+121300	CHEMBL1201260	C02CC01	C
+121913	CHEMBL1165	C09AA13	C
+122015	CHEMBL1201300	V08AA04	V
+125051	CHEMBL1200555	V08AB06	V
+126223	CHEMBL2107885	B01AD07	B
+128277	CHEMBL1004	R06AA09	R
+128278	CHEMBL1004	R06AA59	R
+130830	CHEMBL1183717	R07AB07	R
+132802	CHEMBL1697851	N05AB05	N
+132895	CHEMBL880	J05AB09	J
+132896	CHEMBL880	S01AD07	S
+137721	CHEMBL1618018	S01FA05	S
+137765	CHEMBL17860	N07BC04	N
+142001	CHEMBL1201354	A03AB08	A
+146853	CHEMBL1522	N05CF04	N
+147136	CHEMBL697	N03AD03	N
+148581	CHEMBL1200963	S01EE03	S
+150679	CHEMBL1201474	C10AC02	C
+154653	CHEMBL2106111	A12AA05	A
+159351	CHEMBL2135460	H01BA04	H
+162915	CHEMBL119423	M04AA02	M
+163598	CHEMBL46516	N05AG01	N
+163958	CHEMBL1201347	N01AH05	N
+167008	CHEMBL1201557	L03AB09	L
+167278	CHEMBL1697737	N05BA21	N
+167949	CHEMBL92401	N06AF05	N
+171064	CHEMBL1452696	M01AX18	M
+171065	CHEMBL1452696	M01AX68	M
+171066	CHEMBL1452696	M02AA16	M
+171110	CHEMBL1537	J01CA09	J
+171172	CHEMBL1683	D07AB02	D
+171968	CHEMBL1200386	D07AC18	D
+172342	CHEMBL224325	D08AH02	D
+172343	CHEMBL224325	G01AC03	G
+172344	CHEMBL224325	P01AA04	P
+172345	CHEMBL224325	R02AA11	R
+173054	CHEMBL1200732	D07AC11	D
+173453	CHEMBL251634	C05AA08	C
+173454	CHEMBL251634	D07AC05	D
+173455	CHEMBL251634	D07XC05	D
+173456	CHEMBL251634	H02AB03	H
+176210	CHEMBL842	C03AA04	C
+176211	CHEMBL842	C03AH01	C
+176305	CHEMBL1193	D04AA16	D
+176306	CHEMBL1193	R06AB05	R
+176992	CHEMBL566534	P01BE02	P
+179943	CHEMBL1201496	A10AB05	A
+179944	CHEMBL1201496	A10AD05	A
+180277	CHEMBL1770248	A10BK04	A
+187686	CHEMBL1201548	R05CA01	R
+190295	CHEMBL1201239	V08AA03	V
+193527	CHEMBL1493	G04BD02	G
+193589	CHEMBL1604	J01DB09	J
+193681	CHEMBL2105224	R05DA08	R
+194626	CHEMBL1269025	B01AF03	B
+194682	CHEMBL1359	J01DH03	J
+194773	CHEMBL1455	L01XX03	L
+194901	CHEMBL1736	V08AA02	V
+194957	CHEMBL1950576	S01AA15	S
+195232	CHEMBL455186	L01AB02	L
+202905	CHEMBL1200845	D07AD02	D
+206947	CHEMBL1614637	J01CE05	J
+212905	CHEMBL1201414	B01AB10	B
+212906	CHEMBL1201438	L03AC01	L
+212981	CHEMBL1323	J05AE10	J
+213133	CHEMBL1664	J05AE07	J
+213192	CHEMBL2095212	G04BD12	G
+213210	CHEMBL222813	J05AH01	J
+213229	CHEMBL254316	J05AX08	J
+213267	CHEMBL308954	J05AG04	J
+213297	CHEMBL376359	A10BH04	A
+213371	CHEMBL430	J01MA15	J
+213395	CHEMBL454446	J01DD16	J
+213396	CHEMBL455	D10AF06	D
+213397	CHEMBL455	S01AB04	S
+213420	CHEMBL491571	J01DH04	J
+214704	CHEMBL607400	N03AX23	N
+224427	CHEMBL1231723	C05BB02	C
+226373	CHEMBL1259059	J05AP08	J
+226647	CHEMBL713	J05AF10	J
+227246	CHEMBL1766	D07AC03	D
+227247	CHEMBL1766	D07XC02	D
+227591	CHEMBL493982	B01AC26	B
+240937	CHEMBL959	J05AC02	J
+241604	CHEMBL2110588	A16AX10	A
+243434	CHEMBL1200799	S01EE04	S
+243441	CHEMBL1201262	S01EA02	S
+244470	CHEMBL1200600	C05AA06	C
+244471	CHEMBL1200600	D07AB06	D
+244472	CHEMBL1200600	D07XB04	D
+244473	CHEMBL1200600	D10AA01	D
+244474	CHEMBL1200600	S01BA07	S
+244475	CHEMBL1200600	S01CB05	S
+246101	CHEMBL1201134	A06AX03	A
+262617	CHEMBL1201301	V08CA05	V
+262704	CHEMBL1373254	C03AA07	C
+262854	CHEMBL1908307	C01DB01	C
+264098	CHEMBL1200354	C05BB04	C
+266246	CHEMBL1378024	J04AD01	J
+268446	CHEMBL1201668	C01DX19	C
+275362	CHEMBL1201112	L01BB07	L
+276229	CHEMBL437	D06BA02	D
+276230	CHEMBL437	J01EB07	J
+282544	CHEMBL1661	A06AD12	A
+285463	CHEMBL1540	D06BB06	D
+285464	CHEMBL1540	J05AB13	J
+287996	CHEMBL1191	B05CA04	B
+287997	CHEMBL1191	D06BA04	D
+287998	CHEMBL1191	J01EB02	J
+287999	CHEMBL1191	S01AB01	S
+288997	CHEMBL502835	L01XE31	L
+289819	CHEMBL2023898	J05AP07	J
+289875	CHEMBL3137312	J05AP09	J
+289995	CHEMBL501849	J05AP05	J
+293811	CHEMBL1089221	M02AA11	M
+293812	CHEMBL1089221	S01BC07	S
+295361	CHEMBL1201837	B06AC03	B
+298078	CHEMBL22097	N05CD06	N
+306847	CHEMBL2106123	A12AA13	A
+306864	CHEMBL2356023	A11CC02	A
+320144	CHEMBL75435	M01AE17	M
+320145	CHEMBL75435	M02AA27	M
+320750	CHEMBL1180725	A03AB05	A
+320795	CHEMBL1200592	H02AA03	H
+321126	CHEMBL1463345	C03DA03	C
+321284	CHEMBL1752	R03DA01	R
+321285	CHEMBL1752	R03DA51	R
+321542	CHEMBL37390	R03DA03	R
+321782	CHEMBL517199	C03BA10	C
+330405	CHEMBL942	A06AB02	A
+330406	CHEMBL942	A06AB52	A
+330407	CHEMBL942	A06AG02	A
+330665	CHEMBL1755	C03XA02	C
+331379	CHEMBL258918	G03BB01	G
+332449	CHEMBL3833339	V03AB22	V
+332967	CHEMBL829	R06AD01	R
+345240	CHEMBL1200472	N05CD10	N
+346827	CHEMBL282052	N05CM01	N
+346828	CHEMBL282052	N05CX02	N
+349957	CHEMBL3301675	A06AX04	A
+351571	CHEMBL1201117	M03BA03	M
+351572	CHEMBL1201117	M03BA53	M
+351573	CHEMBL1201117	M03BA73	M
+352759	CHEMBL87992	N03AF04	N
+352788	CHEMBL918	N03AX07	N
+359849	CHEMBL1201075	V08AB12	V
+359928	CHEMBL1256841	N06AF02	N
+359942	CHEMBL1266	R01AA06	R
+359943	CHEMBL1266	R01AB03	R
+359944	CHEMBL1266	S01GA02	S
+359945	CHEMBL1266	S01GA52	S
+359973	CHEMBL1305	R01AC04	R
+359974	CHEMBL1305	R06AX05	R
+359993	CHEMBL1331216	A03BA03	A
+360532	CHEMBL438	D06BA06	D
+360533	CHEMBL438	J01ED07	J
+360645	CHEMBL524004	A03AB12	A
+360757	CHEMBL607710	D01AE07	D
+361895	CHEMBL404422	N05CA03	N
+365009	CHEMBL1599	J01DB08	J
+401564	CHEMBL376488	J04AK05	J
+425665	CHEMBL1201550	L01XX29	L
+456447	CHEMBL1201286	A03AB03	A
+456448	CHEMBL1201286	A03AB53	A
+456974	CHEMBL398440	D08AE05	D
+459038	CHEMBL249837	J01AA05	J
+468149	CHEMBL1200647	G01AA04	G
+468240	CHEMBL1289601	L01XE29	L
+482508	CHEMBL1201567	L03AA02	L
+506610	CHEMBL1374379	R05DB01	R
+508279	CHEMBL314437	N02AX05	N
+509035	CHEMBL1201109	D07AB08	D
+509036	CHEMBL1201109	S01BA11	S
+547931	CHEMBL1201670	L03AA09	L
+547938	CHEMBL1201748	L01CD04	L
+548388	CHEMBL2107816	V10XX03	V
+578924	CHEMBL1201421	S01LA03	S
+587267	CHEMBL435966	P01AB06	P
+616589	CHEMBL1688530	J01XA05	J
+647928	CHEMBL447629	L01AX01	L
+648422	CHEMBL229128	M03BX06	M
+651566	CHEMBL218650	J04AK06	J
+660959	CHEMBL1619785	J01CA15	J
+684112	CHEMBL2103929	J01AA04	J
+703907	CHEMBL3301669	J01XA04	J
+755601	CHEMBL354077	N07XX05	N
+774261	CHEMBL1480987	C04AX01	C
+781123	CHEMBL1200640	B03AA02	B
+781124	CHEMBL1200640	B03AD02	B
+791316	CHEMBL1200862	C02KB01	C
+792289	CHEMBL94394	M01AB17	M
+792290	CHEMBL94394	M02AA09	M
+832291	CHEMBL1201688	S01AD08	S
+889106	CHEMBL2111107	V03AB35	V
+953451	CHEMBL514800	L04AA32	L
+979602	CHEMBL54976	N06AX02	N
+1535290	CHEMBL1200457	V08CA06	V
+1594326	CHEMBL1201191	R06AE09	R
--- a/Data/Input/DISNET/heatmap_target_final_triples.xlsx
+++ b/Data/Input/DISNET/heatmap_target_final_triples.xlsx
--- a/Data/Input/DISNET/score_gdas_csbj.tsv
+++ b/Data/Input/DISNET/score_gdas_csbj.tsv
+	disease_id	gene_id	score	drug_id
+575	C0002395	1636	0.6	CHEMBL1581
--- a/Data/Input/DISNET/score_gdas_csbj_target_filtergen.tsv
+++ b/Data/Input/DISNET/score_gdas_csbj_target_filtergen.tsv
+	disease_id	gene_id	score	drug_id	Original Condition	New Condition	New Condition CUI	Drugs
+0	C0030567	6532	0.04	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+1	C0030567	6532	0.04	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+2	C0030567	6532	0.04	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+3	C0030567	6532	0.04	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+4	C0030567	6532	0.04	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+5	C0006142	2099	0.9	CHEMBL81	Breast cancer	Osteoporosis	C0029456	RALOXIFENE
+6	C0006142	2099	0.9	CHEMBL81	Breast cancer	Osteoporosis	C1962963	RALOXIFENE
+7	C0006142	2099	0.9	CHEMBL81	Breast cancer	Osteoporosis	C2911643	RALOXIFENE
+8	C0006142	2099	0.9	CHEMBL81	Breast cancer	Osteoporosis	C4554622	RALOXIFENE
+9	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+10	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+11	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+12	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+13	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+14	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+15	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+16	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+17	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+18	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+19	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+20	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+21	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+22	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+23	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+24	C0376358	2099	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+25	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Familial adenomatous polyposis	C0032580	CELECOXIB
+26	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colon cancer	C0007102	CELECOXIB
+27	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colon cancer	C0346629	CELECOXIB
+28	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colon cancer	C0699790	CELECOXIB
+29	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colorectal cancer	C0009402	CELECOXIB
+30	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colorectal cancer	C2984278	CELECOXIB
+31	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colorectal cancer	C3542412	CELECOXIB
+32	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Colorectal cancer	C4722085	CELECOXIB
+33	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Lung cancer	C0242379	CELECOXIB
+34	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Lung cancer	C0684249	CELECOXIB
+35	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Lung cancer	C1306460	CELECOXIB
+36	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Breast cancer	C0006142	CELECOXIB
+37	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Breast cancer	C0678222	CELECOXIB
+38	C0003873	5743	0.4	CHEMBL118	Rheumatoid Arthritis	Breast cancer	C3897071	CELECOXIB
+39	C0003873	196	0.37	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0376358	LEFLUNOMIDE
+40	C0003873	196	0.37	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0600139	LEFLUNOMIDE
+41	C0003873	196	0.37	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C2984325	LEFLUNOMIDE
+42	C0003873	196	0.37	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C3541264	LEFLUNOMIDE
+43	C0006142	2100	0.4	CHEMBL81	Breast cancer	Osteoporosis	C0029456	RALOXIFENE
+44	C0006142	2100	0.4	CHEMBL81	Breast cancer	Osteoporosis	C1962963	RALOXIFENE
+45	C0006142	2100	0.4	CHEMBL81	Breast cancer	Osteoporosis	C2911643	RALOXIFENE
+46	C0006142	2100	0.4	CHEMBL81	Breast cancer	Osteoporosis	C4554622	RALOXIFENE
+47	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+48	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+49	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+50	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+51	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+52	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+53	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+54	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+55	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+56	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+57	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+58	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+59	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C0029456	RALOXIFENE
+60	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C1962963	RALOXIFENE
+61	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C2911643	RALOXIFENE
+62	C0376358	2100	0.4	CHEMBL81	Prostate cancer	Osteoporosis	C4554622	RALOXIFENE
+63	C0003873	2185	0.03	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0376358	LEFLUNOMIDE
+64	C0003873	2185	0.03	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0600139	LEFLUNOMIDE
+65	C0003873	2185	0.03	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C2984325	LEFLUNOMIDE
+66	C0003873	2185	0.03	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C3541264	LEFLUNOMIDE
+67	C0003873	2214	0.1	CHEMBL1201572	Rheumatoid arthritis	Asthma	C0004096	ETANERCEPT
+68	C0003873	2214	0.1	CHEMBL1201572	Rheumatoid arthritis	Asthma	C2984299	ETANERCEPT
+69	C0003873	2212	0.4	CHEMBL1201572	Rheumatoid arthritis	Asthma	C0004096	ETANERCEPT
+70	C0003873	2212	0.4	CHEMBL1201572	Rheumatoid arthritis	Asthma	C2984299	ETANERCEPT
+71	C0003873	4049	0.06	CHEMBL1201572	Rheumatoid arthritis	Asthma	C0004096	ETANERCEPT
+72	C0003873	4049	0.06	CHEMBL1201572	Rheumatoid arthritis	Asthma	C2984299	ETANERCEPT
+73	C0003873	3551	0.02	CHEMBL1366	Rheumatoid arthritis	Gastrointestinal stromal tumor	C0238198	AURANOFIN
+74	C0003873	2215	0.1	CHEMBL1201572	Rheumatoid arthritis	Asthma	C0004096	ETANERCEPT
+75	C0003873	2215	0.1	CHEMBL1201572	Rheumatoid arthritis	Asthma	C2984299	ETANERCEPT
+76	C0030567	6530	0.03	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+77	C0030567	6530	0.03	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+78	C0030567	6530	0.03	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+79	C0030567	6530	0.03	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+80	C0030567	6530	0.03	CHEMBL641	Parkinson Disease	ADHD	C1263846	ATOMOXETINE
+81	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+82	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+83	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+84	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+85	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+86	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+87	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+88	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+89	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+90	C0030567	1814	0.1	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+91	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+92	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+93	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+94	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+95	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+96	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+97	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+98	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+99	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+100	C0030567	1812	0.52	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+101	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+102	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+103	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+104	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+105	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+106	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+107	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+108	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+109	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+110	C0030567	1815	0.04	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+111	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+112	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+113	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+114	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+115	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+116	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+117	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+118	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+119	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C0242350	APOMORPHINE
+120	C0030567	3350	0.22	CHEMBL53	Parkinson Disease	Erectile dysfunction	C1961100	APOMORPHINE
+121	C0003873	1723	0.02	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0376358	LEFLUNOMIDE
+122	C0003873	1723	0.02	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C0600139	LEFLUNOMIDE
+123	C0003873	1723	0.02	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C2984325	LEFLUNOMIDE
+124	C0003873	1723	0.02	CHEMBL960	Rheumatoid arthritis	Prostate cancer	C3541264	LEFLUNOMIDE
+125	C0003873	2209	0.02	CHEMBL1201572	Rheumatoid arthritis	Asthma	C0004096	ETANERCEPT
+126	C0003873	2209	0.02	CHEMBL1201572	Rheumatoid arthritis	Asthma	C2984299	ETANERCEPT
--- a/Data/Input/DISNET/score_gdas_one_repodb.tsv
+++ b/Data/Input/DISNET/score_gdas_one_repodb.tsv
+	drug_id	disease_id	gene_id	score
+0	CHEMBL1513	C0004238	185	0.03
+1	CHEMBL1513	C0004238	185	0.03
+2	CHEMBL1513	C0011881	185	0.1
+3	CHEMBL1513	C0011881	185	0.1
+4	CHEMBL1168	C0004238	1636	0.4
+5	CHEMBL1168	C0004238	1636	0.4
+6	CHEMBL191	C0011881	185	0.1
+7	CHEMBL191	C0011881	185	0.1
+8	CHEMBL1560	C0011881	1636	0.4
+9	CHEMBL1560	C0011881	1636	0.4
+10	CHEMBL1560	C0011881	4313	0.03
+11	CHEMBL1560	C0011881	4313	0.03
+12	CHEMBL1560	C0011881	4318	0.08
+13	CHEMBL1560	C0011881	4318	0.08
+14	CHEMBL59	C0020649	1812	0.3
+15	CHEMBL59	C0020649	1813	0.3
+16	CHEMBL1434	C0010674	836	0.02
+17	CHEMBL1434	C0010674	3553	0.1
+18	CHEMBL1434	C0011581	3553	0.4
+19	CHEMBL1434	C0010674	4318	0.03
+20	CHEMBL1434	C0010674	4843	0.1
+21	CHEMBL1434	C0011581	4843	0.33
+22	CHEMBL1434	C0010674	7422	0.03
+23	CHEMBL1434	C0011581	7422	0.37
+24	CHEMBL1393	C0020428	1585	0.4
+25	CHEMBL1393	C0020428	1585	0.4
+26	CHEMBL1393	C0020428	1586	0.13
+27	CHEMBL1393	C0020428	1586	0.13
+28	CHEMBL1393	C0020428	2908	0.2
+29	CHEMBL1393	C0020428	2908	0.2
+30	CHEMBL1393	C0020428	4306	0.12
+31	CHEMBL1393	C0020428	4306	0.12
+32	CHEMBL1017	C0020473	5468	0.05
+33	CHEMBL1017	C0020473	5468	0.05
+34	CHEMBL1042	C0004096	7421	0.1
+35	CHEMBL1042	C0009324	7421	0.03
+36	CHEMBL1042	C0010346	7421	0.08
+37	CHEMBL1042	C0011849	7421	0.1
+38	CHEMBL1042	C0020598	7421	0.16
+39	CHEMBL1042	C0024141	7421	0.06
+40	CHEMBL1042	C0026769	7421	0.4
+41	CHEMBL1042	C0028754	7421	0.1
+42	CHEMBL1042	C0029456	7421	0.7
+43	CHEMBL1042	C0029458	7421	0.1
+44	CHEMBL1040	C0020598	7421	0.16
+45	CHEMBL1536	C0020598	7421	0.16
+46	CHEMBL1536	C0029456	7421	0.7
+47	CHEMBL1536	C0029458	7421	0.1
+48	CHEMBL2356023	C0020598	7421	0.16
+49	CHEMBL846	C0020598	7421	0.16
+50	CHEMBL502	C0026769	590	0.3
+51	CHEMBL502	C0002395	590	0.4
+52	CHEMBL502	C0026769	3553	0.4
+53	CHEMBL502	C0026769	3553	0.4
+54	CHEMBL502	C0002395	3553	0.6
+55	CHEMBL502	C0002395	3553	0.6
+56	CHEMBL502	C0026769	4790	0.03
+57	CHEMBL502	C0002395	4790	0.09
+58	CHEMBL502	C0002395	43	0.4
+59	CHEMBL502	C0002395	3356	0.1
+60	CHEMBL502	C0002395	4842	0.06
+61	CHEMBL502	C0002395	4842	0.06
+62	CHEMBL25	C0003873	595	0.02
+63	CHEMBL25	C0003873	595	0.02
+64	CHEMBL25	C0003873	595	0.02
+65	CHEMBL25	C0026764	595	0.7
+66	CHEMBL25	C0026764	595	0.7
+67	CHEMBL25	C0026764	595	0.7
+68	CHEMBL25	C0003873	834	0.03
+69	CHEMBL25	C0003873	834	0.03
+70	CHEMBL25	C0003873	834	0.03
+71	CHEMBL25	C0003873	834	0.03
+72	CHEMBL25	C0003873	834	0.03
+73	CHEMBL25	C0003873	834	0.03
+74	CHEMBL25	C0004153	834	0.03
+75	CHEMBL25	C0004153	834	0.03
+76	CHEMBL25	C0004153	834	0.03
+77	CHEMBL25	C0004153	834	0.03
+78	CHEMBL25	C0004153	834	0.03
+79	CHEMBL25	C0004153	834	0.03
+80	CHEMBL25	C0003873	836	0.05
+81	CHEMBL25	C0003873	836	0.05
+82	CHEMBL25	C0003873	836	0.05
+83	CHEMBL25	C0003873	836	0.05
+84	CHEMBL25	C0003873	836	0.05
+85	CHEMBL25	C0003873	836	0.05
+86	CHEMBL25	C0004153	836	0.03
+87	CHEMBL25	C0004153	836	0.03
+88	CHEMBL25	C0004153	836	0.03
+89	CHEMBL25	C0004153	836	0.03
+90	CHEMBL25	C0004153	836	0.03
+91	CHEMBL25	C0004153	836	0.03
+92	CHEMBL25	C0026764	836	0.1
+93	CHEMBL25	C0026764	836	0.1
+94	CHEMBL25	C0026764	836	0.1
+95	CHEMBL25	C0026764	836	0.1
+96	CHEMBL25	C0026764	836	0.1
+97	CHEMBL25	C0026764	836	0.1
+98	CHEMBL25	C0003873	1909	0.03
+99	CHEMBL25	C0003873	1909	0.03
+100	CHEMBL25	C0003873	1909	0.03
+101	CHEMBL25	C0004153	1909	0.03
+102	CHEMBL25	C0004153	1909	0.03
+103	CHEMBL25	C0004153	1909	0.03
+104	CHEMBL25	C0026764	1909	0.02
+105	CHEMBL25	C0026764	1909	0.02
+106	CHEMBL25	C0026764	1909	0.02
+107	CHEMBL25	C0003873	4792	0.02
+108	CHEMBL25	C0003873	4792	0.02
+109	CHEMBL25	C0003873	4792	0.02
+110	CHEMBL25	C0026764	4792	0.04
+111	CHEMBL25	C0026764	4792	0.04
+112	CHEMBL25	C0026764	4792	0.04
+113	CHEMBL25	C0003873	5111	0.03
+114	CHEMBL25	C0003873	5111	0.03
+115	CHEMBL25	C0003873	5111	0.03
+116	CHEMBL25	C0026764	5111	0.08
+117	CHEMBL25	C0026764	5111	0.08
+118	CHEMBL25	C0026764	5111	0.08
+119	CHEMBL25	C0003873	5742	0.34
+120	CHEMBL25	C0003873	5742	0.34
+121	CHEMBL25	C0003873	5742	0.34
+122	CHEMBL25	C0004153	5742	0.02
+123	CHEMBL25	C0004153	5742	0.02
+124	CHEMBL25	C0004153	5742	0.02
+125	CHEMBL25	C0026764	5742	0.02
+126	CHEMBL25	C0026764	5742	0.02
+127	CHEMBL25	C0026764	5742	0.02
+128	CHEMBL25	C0003873	5743	0.4
+129	CHEMBL25	C0003873	5743	0.4
+130	CHEMBL25	C0003873	5743	0.4
+131	CHEMBL25	C0004153	5743	0.4
+132	CHEMBL25	C0004153	5743	0.4
+133	CHEMBL25	C0004153	5743	0.4
+134	CHEMBL25	C0026764	5743	0.06
+135	CHEMBL25	C0026764	5743	0.06
+136	CHEMBL25	C0026764	5743	0.06
+137	CHEMBL25	C0003873	7157	0.1
+138	CHEMBL25	C0003873	7157	0.1
+139	CHEMBL25	C0003873	7157	0.1
+140	CHEMBL25	C0003873	7157	0.1
+141	CHEMBL25	C0003873	7157	0.1
+142	CHEMBL25	C0003873	7157	0.1
+143	CHEMBL25	C0004153	7157	0.1
+144	CHEMBL25	C0004153	7157	0.1
+145	CHEMBL25	C0004153	7157	0.1
+146	CHEMBL25	C0004153	7157	0.1
+147	CHEMBL25	C0004153	7157	0.1
+148	CHEMBL25	C0004153	7157	0.1
+149	CHEMBL25	C0004604	7157	0.1
+150	CHEMBL25	C0004604	7157	0.1
+151	CHEMBL25	C0004604	7157	0.1
+152	CHEMBL25	C0004604	7157	0.1
+153	CHEMBL25	C0004604	7157	0.1
+154	CHEMBL25	C0004604	7157	0.1
+155	CHEMBL25	C0026764	7157	0.2
+156	CHEMBL25	C0026764	7157	0.2
+157	CHEMBL25	C0026764	7157	0.2
+158	CHEMBL25	C0026764	7157	0.2
+159	CHEMBL25	C0026764	7157	0.2
+160	CHEMBL25	C0026764	7157	0.2
+161	CHEMBL25	C0004153	4609	0.02
+162	CHEMBL25	C0004153	4609	0.02
+163	CHEMBL25	C0004153	4609	0.02
+164	CHEMBL25	C0026764	4609	0.4
+165	CHEMBL25	C0026764	4609	0.4
+166	CHEMBL25	C0026764	4609	0.4
+167	CHEMBL25	C0026764	3309	0.03
+168	CHEMBL25	C0026764	3309	0.03
+169	CHEMBL25	C0026764	3309	0.03
+170	CHEMBL25	C0026764	6197	0.02
+171	CHEMBL25	C0026764	6197	0.02
+172	CHEMBL25	C0026764	6197	0.02
+173	CHEMBL1373	C0030193	6531	0.02
+174	CHEMBL1175	C0016053	6532	0.04
--- a/Data/Input/DISNET/score_gdas_repodb_target_final.tsv
+++ b/Data/Input/DISNET/score_gdas_repodb_target_final.tsv
--- a/Data/Input/DISNET/score_gdas_two_repodb.tsv
+++ b/Data/Input/DISNET/score_gdas_two_repodb.tsv
+	drug_id	disease_id	gene_id	score
+0	CHEMBL59	C0242422	6531	0.2
+1	CHEMBL1201203	C0242422	6531	0.2
+2	CHEMBL1201236	C0242422	1644	0.31
+3	CHEMBL1237	C0027051	5972	0.4
+4	CHEMBL1237	C0027051	5972	0.4
+5	CHEMBL1237	C0027051	5972	0.4
+6	CHEMBL1237	C0027051	5972	0.4
+7	CHEMBL25	C0027051	834	0.02
+8	CHEMBL25	C0027051	834	0.02
+9	CHEMBL25	C0027051	834	0.02
+10	CHEMBL25	C0027051	834	0.02
+11	CHEMBL25	C0027051	834	0.02
+12	CHEMBL25	C0027051	834	0.02
+13	CHEMBL25	C0029408	834	0.02
+14	CHEMBL25	C0029408	834	0.02
+15	CHEMBL25	C0029408	834	0.02
+16	CHEMBL25	C0029408	834	0.02
+17	CHEMBL25	C0029408	834	0.02
+18	CHEMBL25	C0029408	834	0.02
+19	CHEMBL25	C0027051	836	0.32
+20	CHEMBL25	C0027051	836	0.32
+21	CHEMBL25	C0027051	836	0.32
+22	CHEMBL25	C0027051	836	0.32
+23	CHEMBL25	C0027051	836	0.32
+24	CHEMBL25	C0027051	836	0.32
+25	CHEMBL25	C0029408	836	0.05
+26	CHEMBL25	C0029408	836	0.05
+27	CHEMBL25	C0029408	836	0.05
+28	CHEMBL25	C0029408	836	0.05
+29	CHEMBL25	C0029408	836	0.05
+30	CHEMBL25	C0029408	836	0.05
+31	CHEMBL25	C0027051	1909	0.21
+32	CHEMBL25	C0027051	1909	0.21
+33	CHEMBL25	C0027051	1909	0.21
+34	CHEMBL25	C0030193	1909	0.04
+35	CHEMBL25	C0030193	1909	0.04
+36	CHEMBL25	C0030193	1909	0.04
+37	CHEMBL25	C0149931	1909	0.34
+38	CHEMBL25	C0149931	1909	0.34
+39	CHEMBL25	C0149931	1909	0.34
+40	CHEMBL25	C0027051	3309	0.3
+41	CHEMBL25	C0027051	3309	0.3
+42	CHEMBL25	C0027051	3309	0.3
+43	CHEMBL25	C0029408	3309	0.02
+44	CHEMBL25	C0029408	3309	0.02
+45	CHEMBL25	C0029408	3309	0.02
+46	CHEMBL25	C0027051	5742	0.02
+47	CHEMBL25	C0027051	5742	0.02
+48	CHEMBL25	C0027051	5742	0.02
+49	CHEMBL25	C0030193	5742	0.03
+50	CHEMBL25	C0030193	5742	0.03
+51	CHEMBL25	C0030193	5742	0.03
+52	CHEMBL25	C0027051	5743	0.1
+53	CHEMBL25	C0027051	5743	0.1
+54	CHEMBL25	C0027051	5743	0.1
+55	CHEMBL25	C0029408	5743	0.1
+56	CHEMBL25	C0029408	5743	0.1
+57	CHEMBL25	C0029408	5743	0.1
+58	CHEMBL25	C0030193	5743	0.1
+59	CHEMBL25	C0030193	5743	0.1
+60	CHEMBL25	C0030193	5743	0.1
+61	CHEMBL25	C0948089	5743	0.02
+62	CHEMBL25	C0948089	5743	0.02
+63	CHEMBL25	C0948089	5743	0.02
+64	CHEMBL25	C0027051	7157	0.33
+65	CHEMBL25	C0027051	7157	0.33
+66	CHEMBL25	C0027051	7157	0.33
+67	CHEMBL25	C0027051	7157	0.33
+68	CHEMBL25	C0027051	7157	0.33
+69	CHEMBL25	C0027051	7157	0.33
+70	CHEMBL25	C0029408	7157	0.03
+71	CHEMBL25	C0029408	7157	0.03
+72	CHEMBL25	C0029408	7157	0.03
+73	CHEMBL25	C0029408	7157	0.03
+74	CHEMBL25	C0029408	7157	0.03
+75	CHEMBL25	C0029408	7157	0.03
+76	CHEMBL25	C0030193	7157	0.1
+77	CHEMBL25	C0030193	7157	0.1
+78	CHEMBL25	C0030193	7157	0.1
+79	CHEMBL25	C0030193	7157	0.1
+80	CHEMBL25	C0030193	7157	0.1
+81	CHEMBL25	C0030193	7157	0.1
+82	CHEMBL25	C0032463	7157	0.03
+83	CHEMBL25	C0032463	7157	0.03
+84	CHEMBL25	C0032463	7157	0.03
+85	CHEMBL25	C0032463	7157	0.03
+86	CHEMBL25	C0032463	7157	0.03
+87	CHEMBL25	C0032463	7157	0.03
+88	CHEMBL1581	C0028754	1636	0.3
+89	CHEMBL1581	C0028754	1636	0.3
+90	CHEMBL1168	C0038454	1636	0.4
+91	CHEMBL1168	C0038454	1636	0.4
+92	CHEMBL1017	C0038454	185	0.08
+93	CHEMBL1017	C0038454	185	0.08
+94	CHEMBL1017	C0038454	5468	0.04
+95	CHEMBL1017	C0038454	5468	0.04
+96	CHEMBL1042	C0036341	7421	0.03
+97	CHEMBL1042	C0042870	7421	0.4
+98	CHEMBL502	C0036341	43	0.3
+99	CHEMBL502	C0036341	43	0.3
+100	CHEMBL502	C0030567	43	0.03
+101	CHEMBL502	C0497327	43	0.05
+102	CHEMBL502	C0497327	43	0.05
+103	CHEMBL502	C0036341	3356	0.4
+104	CHEMBL502	C0036341	3356	0.4
+105	CHEMBL502	C0036341	3553	0.4
+106	CHEMBL502	C0036341	3553	0.4
+107	CHEMBL502	C0036341	3553	0.4
+108	CHEMBL502	C0036341	3553	0.4
+109	CHEMBL502	C0026769	3553	0.4
+110	CHEMBL502	C0026769	3553	0.4
+111	CHEMBL502	C0030567	3553	0.28
+112	CHEMBL502	C0030567	3553	0.28
+113	CHEMBL502	C0497327	3553	0.09
+114	CHEMBL502	C0497327	3553	0.09
+115	CHEMBL502	C0497327	3553	0.09
+116	CHEMBL502	C0497327	3553	0.09
+117	CHEMBL502	C0036341	4790	0.1
+118	CHEMBL502	C0036341	4790	0.1
+119	CHEMBL502	C0026769	4790	0.03
+120	CHEMBL502	C0030567	4790	0.03
+121	CHEMBL502	C0036341	4842	0.5
+122	CHEMBL502	C0036341	4842	0.5
+123	CHEMBL502	C0036341	4842	0.5
+124	CHEMBL502	C0036341	4842	0.5
+125	CHEMBL502	C0030567	4842	0.38
+126	CHEMBL502	C0030567	4842	0.38
+127	CHEMBL1373	C0036341	6531	0.4
+128	CHEMBL1373	C1269683	6531	0.04
+129	CHEMBL1175	C0036341	6530	0.34
+130	CHEMBL1175	C1269683	6530	0.4
+131	CHEMBL1175	C0036341	6531	0.4
+132	CHEMBL1175	C0497327	6531	0.33
+133	CHEMBL1175	C1269683	6531	0.04
+134	CHEMBL1175	C0036341	6532	0.4
+135	CHEMBL1175	C0497327	6532	0.02
+136	CHEMBL1175	C1269683	6532	0.6
+137	CHEMBL1536	C0042870	7421	0.4
+138	CHEMBL846	C0042870	7421	0.4
+139	CHEMBL846	C0085682	7421	0.13
+140	CHEMBL2110563	C0042847	4548	0.22
+141	CHEMBL2103737	C0042847	4548	0.22
+142	CHEMBL1040	C0085682	7421	0.13
+143	CHEMBL2356023	C0085682	7421	0.13
+144	CHEMBL502	C0026769	590	0.3
+145	CHEMBL502	C0030567	590	0.02
+146	CHEMBL502	C0497327	590	0.06
+147	CHEMBL502	C0497327	590	0.06
+148	CHEMBL25	C0029408	1645	0.3
+149	CHEMBL25	C0029408	1645	0.3
+150	CHEMBL25	C0029408	1645	0.3
+151	CHEMBL25	C0030193	4792	0.02
+152	CHEMBL25	C0030193	4792	0.02
+153	CHEMBL25	C0030193	4792	0.02
+154	CHEMBL1434	C0031099	4318	0.35
+155	CHEMBL1434	C0032285	4318	0.06
+156	CHEMBL1434	C0031099	7422	0.02
+157	CHEMBL1434	C0032285	7422	0.04
+158	CHEMBL1434	C0042029	7422	0.03
+159	CHEMBL1434	C0032285	240	0.02
+160	CHEMBL1434	C0032285	834	0.32
+161	CHEMBL1434	C0032285	3553	0.6
+162	CHEMBL1434	C0042029	4843	0.3
+163	CHEMBL1229517	C0026764	673	0.49
--- a/Data/Input/DISNET/triplets_total.csv
+++ b/Data/Input/DISNET/triplets_total.csv
+disease_PwB;drug_id;disease_no_PwB
+C0020538;CHEMBL578;C0018802
+C0020538;CHEMBL578;C0018802
+C0020538;CHEMBL577;C0018802
+C0020538;CHEMBL577;C0018802
+C0020538;CHEMBL1513;C0004238
+C0020538;CHEMBL1513;C0011881
+C0020538;CHEMBL1513;C0004238
+C0020538;CHEMBL1513;C0011881
+C0030567;CHEMBL59;C0001206
+C0030567;CHEMBL59;C0020649
+C0030567;CHEMBL59;C0024586
+C0020538;CHEMBL191;C0010674
+C0020538;CHEMBL191;C0011881
+C0020538;CHEMBL191;C0010674
+C0020538;CHEMBL191;C0011881
+C0020538;CHEMBL1168;C0004238
+C0020538;CHEMBL1168;C0004238
+C0020538;CHEMBL1393;C0003962
+C0020538;CHEMBL1393;C0013604
+C0020538;CHEMBL1393;C0020428
+C0020538;CHEMBL1393;C0003962
+C0020538;CHEMBL1393;C0013604
+C0020538;CHEMBL1393;C0020428
+C0020538;CHEMBL1017;C0020473
+C0020538;CHEMBL1017;C0020473
+C0020538;CHEMBL1069;C0018801
+C0020538;CHEMBL1069;C0018801
+C0035579;CHEMBL1042;C0004096
+C0035579;CHEMBL1042;C0009324
+C0035579;CHEMBL1042;C0010346
+C0035579;CHEMBL1042;C0011849
+C0035579;CHEMBL1042;C0020598
+C0035579;CHEMBL1042;C0020626
+C0035579;CHEMBL1042;C0024141
+C0035579;CHEMBL1042;C0026769
+C0035579;CHEMBL1042;C0028754
+C0035579;CHEMBL1042;C0029456
+C0035579;CHEMBL1042;C0029458
+C0030567;CHEMBL1201203;C0015371
+C0035579;CHEMBL1040;C0020598
+C0035579;CHEMBL1040;C0035086
+C0020538;CHEMBL1560;C0011881
+C0020538;CHEMBL1560;C0011881
+C0030567;CHEMBL502;C0002395
+C0030567;CHEMBL502;C0026769
+C0035579;CHEMBL1536;C0020598
+C0035579;CHEMBL1536;C0020626
+C0035579;CHEMBL1536;C0029456
+C0035579;CHEMBL1536;C0029458
+C0035579;CHEMBL2356023;C0020598
+C0035579;CHEMBL2356023;C0020626
+C0035579;CHEMBL846;C0020598
+C0035579;CHEMBL846;C0020626
+C0035579;CHEMBL846;C0035086
+C0026769;CHEMBL25;C0003862
+C0026769;CHEMBL25;C0003873
+C0026769;CHEMBL25;C0004153
+C0026769;CHEMBL25;C0004604
+C0026769;CHEMBL25;C0009443
+C0026769;CHEMBL25;C0026764
+C0026769;CHEMBL25;C0003862
+C0026769;CHEMBL25;C0003873
+C0026769;CHEMBL25;C0004153
+C0026769;CHEMBL25;C0004604
+C0026769;CHEMBL25;C0009443
+C0026769;CHEMBL25;C0026764
+C0026769;CHEMBL25;C0003862
+C0026769;CHEMBL25;C0003873
+C0026769;CHEMBL25;C0004153
+C0026769;CHEMBL25;C0004604
+C0026769;CHEMBL25;C0009443
+C0026769;CHEMBL25;C0026764
+C0026769;CHEMBL1434;C0001144
+C0026769;CHEMBL1434;C0001261
+C0026769;CHEMBL1434;C0003175
+C0026769;CHEMBL1434;C0006277
+C0026769;CHEMBL1434;C0006309
+C0026769;CHEMBL1434;C0010674
+C0026769;CHEMBL1434;C0011581
+C0026769;CHEMBL1434;C0018081
+C0026769;CHEMBL1434;C0023860
+C0030567;CHEMBL1373;C0027404
+C0030567;CHEMBL1373;C0030193
+C0030567;CHEMBL1175;C0016053
+C0030567;CHEMBL59;C0184567
+C0030567;CHEMBL59;C0242422
+C0030567;CHEMBL59;C0600177
+C0030567;CHEMBL59;C1621958
+C0020538;CHEMBL1237;C0027051
+C0020538;CHEMBL1237;C0027051
+C0020538;CHEMBL1237;C0027051
+C0020538;CHEMBL1237;C0027051
+C0020538;CHEMBL1581;C0028754
+C0020538;CHEMBL1581;C0028754
+C0020538;CHEMBL1168;C0038454
+C0020538;CHEMBL1168;C0038454
+C0020538;CHEMBL1017;C0038454
+C0020538;CHEMBL1017;C0038454
+C0035579;CHEMBL1042;C0036337
+C0035579;CHEMBL1042;C0036341
+C0035579;CHEMBL1042;C0042870
+C0030567;CHEMBL1201203;C0242422
+C0020538;CHEMBL611;C1739363
+C0002892;CHEMBL2110563;C0042847
+C0002892;CHEMBL2110563;C0162316
+C0002892;CHEMBL2103737;C0006114
+C0002892;CHEMBL2103737;C0033860
+C0002892;CHEMBL2103737;C0042847
+C0035579;CHEMBL1040;C0085682
+C0030567;CHEMBL1201236;C0184567
+C0030567;CHEMBL1201236;C0242422
+C0002395;CHEMBL502;C0026769
+C0002395;CHEMBL502;C0030567
+C0002395;CHEMBL502;C0036341
+C0002395;CHEMBL502;C0497327
+C0030567;CHEMBL502;C0036341
+C0030567;CHEMBL502;C0497327
+C0035579;CHEMBL1536;C0042870
+C0035579;CHEMBL1536;C3536984
+C0035579;CHEMBL2356023;C0039621
+C0035579;CHEMBL2356023;C0085682
+C0035579;CHEMBL846;C0042870
+C0035579;CHEMBL846;C0085682
+C0035579;CHEMBL846;C1527383
+C0026769;CHEMBL25;C0027051
+C0026769;CHEMBL25;C0029408
+C0026769;CHEMBL25;C0030193
+C0026769;CHEMBL25;C0032463
+C0026769;CHEMBL25;C0040460
+C0026769;CHEMBL25;C0149931
+C0026769;CHEMBL25;C0393735
+C0026769;CHEMBL25;C0948089
+C0026769;CHEMBL25;C0027051
+C0026769;CHEMBL25;C0029408
+C0026769;CHEMBL25;C0030193
+C0026769;CHEMBL25;C0032463
+C0026769;CHEMBL25;C0040460
+C0026769;CHEMBL25;C0149931
+C0026769;CHEMBL25;C0393735
+C0026769;CHEMBL25;C0948089
+C0026769;CHEMBL25;C0027051
+C0026769;CHEMBL25;C0029408
+C0026769;CHEMBL25;C0030193
+C0026769;CHEMBL25;C0032463
+C0026769;CHEMBL25;C0040460
+C0026769;CHEMBL25;C0149931
+C0026769;CHEMBL25;C0393735
+C0026769;CHEMBL25;C0948089
+C0026769;CHEMBL1434;C0031099
+C0026769;CHEMBL1434;C0031350
+C0026769;CHEMBL1434;C0032064
+C0026769;CHEMBL1434;C0032285
+C0026769;CHEMBL1434;C0034362
+C0026769;CHEMBL1434;C0035854
+C0026769;CHEMBL1434;C0037199
+C0026769;CHEMBL1434;C0039128
+C0026769;CHEMBL1434;C0042029
+C0025202;CHEMBL1229517;C0026764
+C0030567;CHEMBL1373;C0036341
+C0030567;CHEMBL1373;C1269683
+C0030567;CHEMBL1175;C0036341
+C0030567;CHEMBL1175;C0497327
+C0030567;CHEMBL1175;C1269683
+C0020538;CHEMBL1581;C0002395
--- a/Data/Output/count_drug_target.csv
+++ b/Data/Output/count_drug_target.csv
+ATC_LEVEL
+23.52959366689344
+17.61704496258272
+12.499226915702888
+11.933329210217082
+11.181891273424455
+8.831715010204713
+8.578143360752057
+1.7255241511534416
+1.6513080586307132
+1.2121961778712351
+0.5968210773702765
+0.547343682355124
+0.07421609252272868
+0.0216463603191292
--- a/Notebooks/Results/.ipynb_checkpoints/1.1 Patterns DREBIOP - Pathway-based treated disease-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/1.1 Patterns DREBIOP - Pathway-based treated disease-checkpoint.ipynb
+{
+ "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 sqlalchemy import create_engine\n",
+    "from sklearn import preprocessing\n",
+    "import mysql.connector\n",
+    "from pandas import DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = triplets_total.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_id = triplets_total[\"disease_PwB\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert(lista):\n",
+    "    return tuple(i for i in lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0030567', 'C0030567', 'C0030567', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0030567', 'C0035579', 'C0035579', 'C0020538', 'C0030567', 'C0030567', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0020538', 'C0020538', 'C0020538', 'C0020538', 'C0035579', 'C0035579', 'C0035579', 'C0030567', 'C0020538', 'C0002892', 'C0002892', 'C0002892', 'C0002892', 'C0002892', 'C0035579', 'C0030567', 'C0030567', 'C0002395', 'C0002395', 'C0002395', 'C0002395', 'C0030567', 'C0030567', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0035579', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0026769', 'C0025202', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0030567', 'C0020538')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Driver function\n",
+    "lista = disease_id\n",
+    "print(convert(lista))\n",
+    "list_disease_id = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen = f'''SELECT disease_id, gene_id FROM disnet_biolayer.disease_gene\n",
+    "where sio_id != 'SIO_001120'\n",
+    "and sio_id != 'NO_CURATED'\n",
+    "and disease_id IN {list_disease_id}\n",
+    ";'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen=pd.read_sql(dis_gen, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_num_gen = dis_gen.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_num_gen = dis_gen_num_gen.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "581.8571428571429"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_num_gen[\"gene_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       7.000000\n",
+       "mean      581.857143\n",
+       "std       464.301780\n",
+       "min         3.000000\n",
+       "25%       267.500000\n",
+       "50%       559.000000\n",
+       "75%       854.500000\n",
+       "max      1267.000000\n",
+       "Name: gene_id, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_num_gen[\"gene_id\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### pw via gene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = f'''\n",
+    "SELECT dg.disease_id,gp.pathway_id\n",
+    "        FROM disnet_biolayer.disease_gene dg \n",
+    "    JOIN disnet_biolayer.disease ds ON ds.disease_id = dg.disease_id\n",
+    "    JOIN disnet_biolayer.tmp_gene_pathway gp ON gp.gene_id = dg.gene_id\n",
+    "    where dg.disease_id in {list_disease_id}\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw=pd.read_sql(dis_gen_pw, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = dis_gen_pw.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = dis_gen_pw.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1847.142857142857"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_pw[\"pathway_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       7.000000\n",
+       "mean     1847.142857\n",
+       "std      1531.396251\n",
+       "min         3.000000\n",
+       "25%       795.000000\n",
+       "50%      1640.000000\n",
+       "75%      2748.500000\n",
+       "max      4200.000000\n",
+       "Name: pathway_id, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_pw[\"pathway_id\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### pw direct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_path_direct = pd.read_csv('./Data/Input/DISNET/disease_pathway.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total_fil = triplets_total.drop([\"disease_no_PwB\",\"drug_id\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total_fil = triplets_total_fil.rename(columns={\"disease_PwB\": \"disease_id\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = triplets_total_fil.merge(dis_path_direct,on=\"disease_id\",how= \"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = dr_pw.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = dr_pw.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dr_pw[\"pathway_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### drug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = f'''SELECT disease_id,drug_id FROM disnet_drugslayer.drug_disease\n",
+    "where disease_id in {list_disease_id}'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug=pd.read_sql(drug, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = drug.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = drug.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "685.5714285714286"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drug[\"drug_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### symptom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint = f'''SELECT DISTINCT\n",
+    "        ds.disease_id,s.cui as symptom \n",
+    "        FROM disnet_biolayer.disease ds \n",
+    "            JOIN edsssdb.layersmappings lm on ds.disease_id = lm.cui\n",
+    "            JOIN edsssdb.disease_symptom dsy ON lm.disnet_id = dsy.disease_id\n",
+    "            JOIN edsssdb.symptom s ON dsy.cui = s.cui\n",
+    "       where ds.disease_id in {list_disease_id}\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all=pd.read_sql(sint, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all = sint_all.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all = sint_all.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83.71428571428571"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sint_all[\"symptom\"].mean()"
+   ]
+  }
+ ],
+ "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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/1.2 Patterns DREBIOP - Target gene-based treated disease-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/1.2 Patterns DREBIOP - Target gene-based treated disease-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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 sqlalchemy import create_engine\n",
+    "from sklearn import preprocessing\n",
+    "import mysql.connector\n",
+    "from pandas import DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = triplets_total.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_id = triplets_total[\"disease_no_PwB\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert(lista):\n",
+    "    return tuple(i for i in lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C0018802', 'C0018802', 'C0004238', 'C0011881', 'C0001206', 'C0020649', 'C0024586', 'C0010674', 'C0011881', 'C0004238', 'C0003962', 'C0013604', 'C0020428', 'C0020473', 'C0018801', 'C0004096', 'C0009324', 'C0010346', 'C0011849', 'C0020598', 'C0020626', 'C0024141', 'C0026769', 'C0028754', 'C0029456', 'C0029458', 'C0015371', 'C0020598', 'C0035086', 'C0011881', 'C0002395', 'C0026769', 'C0020598', 'C0020626', 'C0029456', 'C0029458', 'C0020598', 'C0020626', 'C0020598', 'C0020626', 'C0035086', 'C0003862', 'C0003873', 'C0004153', 'C0004604', 'C0009443', 'C0026764', 'C0001144', 'C0001261', 'C0003175', 'C0006277', 'C0006309', 'C0010674', 'C0011581', 'C0018081', 'C0023860', 'C0027404', 'C0030193', 'C0016053', 'C0184567', 'C0242422', 'C0600177', 'C1621958', 'C0027051', 'C0028754', 'C0038454', 'C0038454', 'C0036337', 'C0036341', 'C0042870', 'C0242422', 'C1739363', 'C0042847', 'C0162316', 'C0006114', 'C0033860', 'C0042847', 'C0085682', 'C0184567', 'C0242422', 'C0026769', 'C0030567', 'C0036341', 'C0497327', 'C0036341', 'C0497327', 'C0042870', 'C3536984', 'C0039621', 'C0085682', 'C0042870', 'C0085682', 'C1527383', 'C0027051', 'C0029408', 'C0030193', 'C0032463', 'C0040460', 'C0149931', 'C0393735', 'C0948089', 'C0031099', 'C0031350', 'C0032064', 'C0032285', 'C0034362', 'C0035854', 'C0037199', 'C0039128', 'C0042029', 'C0026764', 'C0036341', 'C1269683', 'C0036341', 'C0497327', 'C1269683', 'C0002395')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Driver function\n",
+    "lista = disease_id\n",
+    "print(convert(lista))\n",
+    "list_disease_id = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen = f'''SELECT disease_id, gene_id FROM disnet_biolayer.disease_gene\n",
+    "where sio_id != 'SIO_001120'\n",
+    "and sio_id != 'NO_CURATED'\n",
+    "and disease_id IN {list_disease_id}\n",
+    ";'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen=pd.read_sql(dis_gen, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_num_gen = dis_gen.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_num_gen = dis_gen_num_gen.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "228.91780821917808"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_num_gen[\"gene_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      73.000000\n",
+       "mean      228.917808\n",
+       "std       301.082480\n",
+       "min         1.000000\n",
+       "25%        14.000000\n",
+       "50%        79.000000\n",
+       "75%       392.000000\n",
+       "max      1369.000000\n",
+       "Name: gene_id, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_num_gen[\"gene_id\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### pw via gene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = f'''\n",
+    "SELECT dg.disease_id,gp.pathway_id\n",
+    "        FROM disnet_biolayer.disease_gene dg \n",
+    "    JOIN disnet_biolayer.disease ds ON ds.disease_id = dg.disease_id\n",
+    "    JOIN disnet_biolayer.tmp_gene_pathway gp ON gp.gene_id = dg.gene_id\n",
+    "    where dg.disease_id in {list_disease_id}\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw=pd.read_sql(dis_gen_pw, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = dis_gen_pw.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_gen_pw = dis_gen_pw.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "766.2361111111111"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dis_gen_pw[\"pathway_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### pw direct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dis_path_direct = pd.read_csv('./Data/Input/DISNET/disease_pathway.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total_fil = triplets_total.drop([\"disease_PwB\",\"drug_id\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total_fil = triplets_total_fil.rename(columns={\"disease_no_PwB\": \"disease_id\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = triplets_total_fil.merge(dis_path_direct,on=\"disease_id\",how= \"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = dr_pw.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr_pw = dr_pw.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.4"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dr_pw[\"pathway_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### drug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = f'''SELECT disease_id,drug_id FROM disnet_drugslayer.drug_disease\n",
+    "where disease_id in {list_disease_id}'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug=pd.read_sql(drug, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = drug.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug = drug.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "441.88607594936707"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drug[\"drug_id\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### sintomas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint = f'''SELECT DISTINCT\n",
+    "        ds.disease_id,s.cui as symptom \n",
+    "        FROM disnet_biolayer.disease ds \n",
+    "            JOIN edsssdb.layersmappings lm on ds.disease_id = lm.cui\n",
+    "            JOIN edsssdb.disease_symptom dsy ON lm.disnet_id = dsy.disease_id\n",
+    "            JOIN edsssdb.symptom s ON dsy.cui = s.cui\n",
+    "       where ds.disease_id in {list_disease_id}\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all=pd.read_sql(sint, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all = sint_all.groupby(['disease_id']).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sint_all = sint_all.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "76.81333333333333"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sint_all[\"symptom\"].mean()"
+   ]
+  }
+ ],
+ "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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/2. GDAs pathways DREGE-DREBIOP-DISNET-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/2. GDAs pathways DREGE-DREBIOP-DISNET-checkpoint.ipynb
+{
+ "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('./Data/Input/DISNET/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(\"./Data/Input/DISNET/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('./Data/Input/DISNET/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('./Data/Input/DISNET/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('./Data/Input/DISNET/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('./Data/Input/DISNET/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(\"./Data/Input/DISNET/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": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAS7ElEQVR4nO3dcazd5X3f8fenhlBvCYszLsi13dmLnK0GLU7xPGvZJhqq4ZA/TKRGcjYFVCE5Y2RKpf5Rkz+WVJMlKjXNhjaonAZhpi7IWtLhNdDNZc2yqIB7iRyMISxeYHBjC98k60I6icnmuz/Ow3Rkju891/fec3P9vF/S0fmd7+95zu95ZOvjn5/zO7+TqkKS1IefWekBSJImx9CXpI4Y+pLUEUNfkjpi6EtSR65Y6QHM55prrqnNmzev9DAkaVV55plnflBVUxfWf+pDf/PmzUxPT6/0MCRpVUnyP0fVXd6RpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SO/NR/I3cxNu//2ooc9+V7P7Iix5Wk+XimL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHZk39JP8bJJjSb6d5GSS32z1zyX5fpLj7XHrUJ97kpxK8mKSW4bqNyY50fbdlyTLMy1J0ijj3IbhDeBDVfWTJFcC30zyeNv3har67eHGSbYBe4HrgZ8D/jjJ+6rqPPAAsA94CngM2A08jiRpIuY906+Bn7SXV7ZHzdFlD/BIVb1RVS8Bp4CdSdYDV1fVk1VVwMPAbYsavSRpQcZa00+yJslx4CxwtKqebrs+leTZJA8mWddqG4BXh7rPtNqGtn1hfdTx9iWZTjI9Ozs7/mwkSXMaK/Sr6nxVbQc2Mjhrv4HBUs17ge3AGeDzrfmodfqaoz7qeAerakdV7ZiamhpniJKkMSzo6p2q+nPg68Duqnqt/WPwJvBFYGdrNgNsGuq2ETjd6htH1CVJEzLO1TtTSd7dttcCvwx8p63Rv+WjwHNt+wiwN8lVSbYAW4FjVXUGeD3JrnbVzu3Ao0s3FUnSfMa5emc9cCjJGgb/SByuqj9M8m+TbGewRPMy8EmAqjqZ5DDwPHAOuLtduQNwF/AQsJbBVTteuSNJEzRv6FfVs8AHRtQ/MUefA8CBEfVp4IYFjlGStET8Rq4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUkXlDP8nPJjmW5NtJTib5zVZ/T5KjSb7bntcN9bknyakkLya5Zah+Y5ITbd99SbI805IkjTLOmf4bwIeq6v3AdmB3kl3AfuCJqtoKPNFek2QbsBe4HtgN3J9kTXuvB4B9wNb22L10U5EkzWfe0K+Bn7SXV7ZHAXuAQ61+CLitbe8BHqmqN6rqJeAUsDPJeuDqqnqyqgp4eKiPJGkCxlrTT7ImyXHgLHC0qp4GrquqMwDt+drWfAPw6lD3mVbb0LYvrI863r4k00mmZ2dnFzAdSdJcxgr9qjpfVduBjQzO2m+Yo/modfqaoz7qeAerakdV7ZiamhpniJKkMSzo6p2q+nPg6wzW4l9rSza057Ot2QywaajbRuB0q28cUZckTcg4V+9MJXl3214L/DLwHeAIcEdrdgfwaNs+AuxNclWSLQw+sD3WloBeT7KrXbVz+1AfSdIEXDFGm/XAoXYFzs8Ah6vqD5M8CRxOcifwCvAxgKo6meQw8DxwDri7qs6397oLeAhYCzzeHpKkCZk39KvqWeADI+o/BG6+SJ8DwIER9Wlgrs8DJEnLyG/kSlJHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyDg/jL4pyZ8keSHJySSfbvXPJfl+kuPtcetQn3uSnEryYpJbhuo3JjnR9t3XfiBdkjQh4/ww+jng16vqW0neBTyT5Gjb94Wq+u3hxkm2AXuB64GfA/44yfvaj6M/AOwDngIeA3bjj6NL0sTMe6ZfVWeq6ltt+3XgBWDDHF32AI9U1RtV9RJwCtiZZD1wdVU9WVUFPAzcttgJSJLGt6A1/SSbgQ8AT7fSp5I8m+TBJOtabQPw6lC3mVbb0LYvrEuSJmTs0E/yTuArwK9V1Y8ZLNW8F9gOnAE+/1bTEd1rjvqoY+1LMp1kenZ2dtwhSpLmMVboJ7mSQeD/flV9FaCqXquq81X1JvBFYGdrPgNsGuq+ETjd6htH1N+mqg5W1Y6q2jE1NbWQ+UiS5jDO1TsBvgS8UFW/M1RfP9Tso8BzbfsIsDfJVUm2AFuBY1V1Bng9ya72nrcDjy7RPCRJYxjn6p0PAp8ATiQ53mqfAT6eZDuDJZqXgU8CVNXJJIeB5xlc+XN3u3IH4C7gIWAtg6t2vHJHkiZo3tCvqm8yej3+sTn6HAAOjKhPAzcsZICSpKXjN3IlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6Ms6tlaV5bd7/tRU79sv3fmTFji2tNp7pS1JHDH1J6oihL0kdMfQlqSOGviR1ZN7QT7IpyZ8keSHJySSfbvX3JDma5Lvted1Qn3uSnEryYpJbhuo3JjnR9t2XZNRv70qSlsk4Z/rngF+vql8AdgF3J9kG7AeeqKqtwBPtNW3fXuB6YDdwf5I17b0eAPYBW9tj9xLORZI0j3lDv6rOVNW32vbrwAvABmAPcKg1OwTc1rb3AI9U1RtV9RJwCtiZZD1wdVU9WVUFPDzUR5I0AQta00+yGfgA8DRwXVWdgcE/DMC1rdkG4NWhbjOttqFtX1gfdZx9SaaTTM/Ozi5kiJKkOYwd+kneCXwF+LWq+vFcTUfUao7624tVB6tqR1XtmJqaGneIkqR5jBX6Sa5kEPi/X1VfbeXX2pIN7flsq88Am4a6bwROt/rGEXVJ0oSMc/VOgC8BL1TV7wztOgLc0bbvAB4dqu9NclWSLQw+sD3WloBeT7KrveftQ30kSRMwzg3XPgh8AjiR5HirfQa4Fzic5E7gFeBjAFV1Mslh4HkGV/7cXVXnW7+7gIeAtcDj7SFJmpB5Q7+qvsno9XiAmy/S5wBwYER9GrhhIQOUJC0dv5ErSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdWTe0E/yYJKzSZ4bqn0uyfeTHG+PW4f23ZPkVJIXk9wyVL8xyYm2774kF/vdXUnSMhnnTP8hYPeI+heqant7PAaQZBuwF7i+9bk/yZrW/gFgH7C1PUa9pyRpGc0b+lX1DeBHY77fHuCRqnqjql4CTgE7k6wHrq6qJ6uqgIeB2y5xzJKkS7SYNf1PJXm2Lf+sa7UNwKtDbWZabUPbvrA+UpJ9SaaTTM/Ozi5iiJKkYZca+g8A7wW2A2eAz7f6qHX6mqM+UlUdrKodVbVjamrqEocoSbrQJYV+Vb1WVeer6k3gi8DOtmsG2DTUdCNwutU3jqhLkibokkK/rdG/5aPAW1f2HAH2JrkqyRYGH9geq6ozwOtJdrWrdm4HHl3EuCVJl+CK+Rok+TJwE3BNkhngs8BNSbYzWKJ5GfgkQFWdTHIYeB44B9xdVefbW93F4EqgtcDj7SFJmqB5Q7+qPj6i/KU52h8ADoyoTwM3LGh0kqQl5TdyJakjhr4kdcTQl6SOzLumr9Vl8/6vrfQQJP0U80xfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHVk3tBP8mCSs0meG6q9J8nRJN9tz+uG9t2T5FSSF5PcMlS/McmJtu++9gPpkqQJGudM/yFg9wW1/cATVbUVeKK9Jsk2YC9wfetzf5I1rc8DwD5ga3tc+J6SpGU2b+hX1TeAH11Q3gMcatuHgNuG6o9U1RtV9RJwCtiZZD1wdVU9WVUFPDzUR5I0IZe6pn9dVZ0BaM/XtvoG4NWhdjOttqFtX1iXJE3QUn+QO2qdvuaoj36TZF+S6STTs7OzSzY4SerdpYb+a23JhvZ8ttVngE1D7TYCp1t944j6SFV1sKp2VNWOqampSxyiJOlClxr6R4A72vYdwKND9b1JrkqyhcEHtsfaEtDrSXa1q3ZuH+ojSZqQK+ZrkOTLwE3ANUlmgM8C9wKHk9wJvAJ8DKCqTiY5DDwPnAPurqrz7a3uYnAl0Frg8faQJE3QvKFfVR+/yK6bL9L+AHBgRH0auGFBo5MkLSm/kStJHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1ZFGhn+TlJCeSHE8y3WrvSXI0yXfb87qh9vckOZXkxSS3LHbwkqSFWYoz/V+qqu1VtaO93g88UVVbgSfaa5JsA/YC1wO7gfuTrFmC40uSxrQcyzt7gENt+xBw21D9kap6o6peAk4BO5fh+JKki1hs6Bfwn5M8k2Rfq11XVWcA2vO1rb4BeHWo70yrvU2SfUmmk0zPzs4ucoiSpLdcscj+H6yq00muBY4m+c4cbTOiVqMaVtVB4CDAjh07RraRJC3cos70q+p0ez4L/AGD5ZrXkqwHaM9nW/MZYNNQ943A6cUcX5K0MJcc+kn+cpJ3vbUN/EPgOeAIcEdrdgfwaNs+AuxNclWSLcBW4NilHl+StHCLWd65DviDJG+9z7+rqj9K8mfA4SR3Aq8AHwOoqpNJDgPPA+eAu6vq/KJGL0lakEsO/ar6HvD+EfUfAjdfpM8B4MClHlOStDh+I1eSOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjqy2LtsSt3avP9rK3bsl+/9yIodW6ubZ/qS1BFDX5I6YuhLUkcMfUnqiB/kLoOV/IBPkuZi6GvV8x9ZaXwu70hSRwx9SeqIoS9JHZn4mn6S3cC/AtYAv1dV9056DNJqt1KfY/hN4NVvomf6SdYA/wb4MLAN+HiSbZMcgyT1bNJn+juBU1X1PYAkjwB7gOcnPA5Jl6DHK6Uut//dTDr0NwCvDr2eAf7OhY2S7AP2tZc/SfLiPO97DfCDJRnh6uK8++K8V0B+a6WOvOh5/7VRxUmHfkbU6m2FqoPAwbHfNJmuqh2LGdhq5Lz74rz7slzznvTVOzPApqHXG4HTEx6DJHVr0qH/Z8DWJFuSvAPYCxyZ8BgkqVsTXd6pqnNJPgX8JwaXbD5YVSeX4K3HXgq6zDjvvjjvvizLvFP1tiV1SdJlym/kSlJHDH1J6siqCf0ku5O8mORUkv0j9ifJfW3/s0l+cSXGudTGmPc/bvN9NsmfJnn/SoxzOcw396F2fzvJ+SS/MsnxLZdx5p3kpiTHk5xM8l8nPcblMMbf9b+S5D8m+Xab96+uxDiXUpIHk5xN8txF9i99rlXVT/2DwYe+/wP468A7gG8D2y5ocyvwOIPvAuwCnl7pcU9o3n8XWNe2P3w5zHvcuQ+1+y/AY8CvrPS4J/Rn/m4G32L/+fb62pUe94Tm/Rngt9r2FPAj4B0rPfZFzvsfAL8IPHeR/Uuea6vlTP//376hqv4v8NbtG4btAR6ugaeAdydZP+mBLrF5511Vf1pV/6u9fIrBdx8uB+P8mQP8M+ArwNlJDm4ZjTPvfwR8tapeAaiqy2Hu48y7gHclCfBOBqF/brLDXFpV9Q0G87iYJc+11RL6o27fsOES2qw2C53TnQzOCi4H8849yQbgo8DvTnBcy22cP/P3AeuSfD3JM0lun9jols848/7XwC8w+ELnCeDTVfXmZIa3YpY811bLzyWOc/uGsW7xsMqMPackv8Qg9P/eso5ocsaZ+78EfqOqzg9O/i4L48z7CuBG4GZgLfBkkqeq6r8v9+CW0TjzvgU4DnwIeC9wNMl/q6ofL/PYVtKS59pqCf1xbt9wOd7iYaw5JflbwO8BH66qH05obMttnLnvAB5pgX8NcGuSc1X1HyYywuUx7t/1H1TVXwB/keQbwPuB1Rz648z7V4F7a7DYfSrJS8DfBI5NZogrYslzbbUs74xz+4YjwO3t0+5dwP+uqjOTHugSm3feSX4e+CrwiVV+pneheedeVVuqanNVbQb+PfBPV3ngw3h/1x8F/n6SK5L8JQZ3qn1hwuNcauPM+xUG/7shyXXA3wC+N9FRTt6S59qqONOvi9y+Ick/aft/l8HVG7cCp4D/w+CsYFUbc97/HPirwP3tjPdcXQZ3JBxz7pedceZdVS8k+SPgWeBNBr9AN/KSv9VizD/vfwE8lOQEg2WP36iqVX2r6SRfBm4CrkkyA3wWuBKWL9e8DYMkdWS1LO9IkpaAoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I68v8AhUScvVnJ3toAAAAASUVORK5CYII=\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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/3.1 Type of drugs - MESHPA-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/3.1 Type of drugs - MESHPA-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_drugs = Triples_target_final[\"drug_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_drugs = pd.DataFrame(target_drugs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "267"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(target_drugs[\"drug_id\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = triplets_total.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathways_drug = triplets_total[\"drug_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathways_drug = pd.DataFrame(pathways_drug)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## union de los farmacos con su tipo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_drug =pd.read_excel(\"./Data/Input/DISNET/Drug_Categories.xlsx\", engine='openpyxl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cases_type_drug = type_drug.merge(target_drugs, how='inner', on='drug_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "237"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(cases_type_drug[\"drug_id\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cases_target = cases_type_drug.drop([\"class_id\",\"drug_name\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug_target = pd.value_counts(cases_target[\"class_name\"],normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type_target = count_drug_target.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type_target[\"Drug_repositioning_type\"] = \"DREGE\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug_target.to_csv(\"count_drug_target.csv\",index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cases = pathways_drug.merge(type_drug, how='inner', on='drug_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug = pd.value_counts(cases[\"class_name\"],normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type = count_drug.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type[\"Drug_repositioning_type\"] = \"DREBIOP\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_all = pd.concat([drug_type,drug_type_target])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 12))\n",
+    "my_colors = [\"#4ea8af\",\"#d76b73\"] \n",
+    "sns.set_palette( my_colors ) \n",
+    "sns.barplot(data=type_all, x=\"class_name\", y=\"index\", hue=\"Drug_repositioning_type\", orient='h')\n",
+    "#plt.xticks(rotation=90)\n",
+    "sns.despine(top=False, bottom=True)\n",
+    "plt.xlabel(\"\")\n",
+    "plt.ylabel(\"\")\n",
+    "ax.legend(loc=(0.6,0), title='Type of Drug Repositioning', fontsize=12)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plot_Drugs_Type.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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/3.2 Types of drugs - ATC-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/3.2 Types of drugs - ATC-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_drugs = Triples_target_final[\"drug_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_drugs = pd.DataFrame(target_drugs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplets_total = triplets_total.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathways_drug = triplets_total[\"drug_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathways_drug = pd.DataFrame(pathways_drug)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_atc = pd.read_csv('./Data/Input/DISNET/drug_atc.tsv', sep='\\t')\n",
+    "drug_atc = drug_atc.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atc_cases_target = drug_atc.merge(target_drugs, how='right', on='drug_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atc_cases_target = atc_cases_target.drop([\"ATC_code_id\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#atc_cases_target = atc_cases_target.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug_target = pd.value_counts(atc_cases_target[\"ATC_LEVEL\"],normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type_target = count_drug_target.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type_target[\"Drug_repositioning_type\"] = \"DREGE\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug_target.to_csv(\"count_drug_target.csv\",index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atc_cases = pathways_drug.merge(drug_atc, how='inner', on='drug_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_drug = pd.value_counts(atc_cases[\"ATC_LEVEL\"],normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type = count_drug.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drug_type[\"Drug_repositioning_type\"] = \"DREBIOP\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_atc_all = pd.concat([drug_type,drug_type_target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atc_name = pd.read_excel(\"./Data/Input/DISNET/ATC_desc_name.xlsx\")\n",
+    "atc_name['index'] = atc_name['index'].str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type_atc_name = type_atc_all.merge(atc_name, on=\"index\", how= \"left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 12))\n",
+    "my_colors = [\"#4ea8af\",\"#d76b73\"] \n",
+    "sns.set_palette( my_colors )\n",
+    "sns.barplot(data=type_atc_name, x=\"ATC_LEVEL\", y=\"Type\", hue=\"Drug_repositioning_type\", orient='h')\n",
+    "#plt.xticks(rotation=90)\n",
+    "sns.despine(top=False, bottom=True)\n",
+    "plt.xlabel(\"\")\n",
+    "plt.ylabel(\"\")\n",
+    "ax.legend(loc=(0.6,0), title='Type of Drug Repositioning', fontsize=12)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plot_Drugs_Type_ATC.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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/4.1 Types of diseases - DREGE-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/4.1 Types of diseases - DREGE-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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 sqlalchemy import create_engine\n",
+    "from sklearn import preprocessing\n",
+    "import mysql.connector\n",
+    "from pandas import DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final_ = Triples_target_final.drop([\"score\",\"gene_id\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Triples_target_final_ = Triples_target_final_.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_id = Triples_target_final_[\"disease_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_id = disease_id.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_new = Triples_target_final_[\"disease_new\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_new = disease_new.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert(list):\n",
+    "    return tuple(i for i in list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C0030567', 'C0006142', 'C0376358', 'C0003873', 'C0007134', 'C0027819', 'C0020473', 'C0004153', 'C0020474', 'C0028754', 'C0028768', 'C0005587', 'C0036341', 'C0005586', 'C0038436', 'C0003125', 'C0014544', 'C0003469', 'C0002395', 'C0020538', 'C1140680', 'C0040517', 'C0021390', 'C0026769', 'C0024623', 'C0035579', 'C0011849', 'C0023434', 'C0010674', 'C0025202', 'C0002736', 'C0017205', 'C0010308')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Driver function\n",
+    "lista = disease_id\n",
+    "print(convert(lista))\n",
+    "list_disease_id = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C1263846', 'C0029456', 'C1962963', 'C2911643', 'C4554622', 'C0032580', 'C0007102', 'C0346629', 'C0699790', 'C0009402', 'C2984278', 'C3542412', 'C4722085', 'C0242379', 'C0684249', 'C1306460', 'C0006142', 'C0678222', 'C3897071', 'C0376358', 'C0600139', 'C2984325', 'C3541264', 'C0004096', 'C2984299', 'C0238198', 'C0242350', 'C1961100', 'C0003873', 'C0004153', 'C0006413', 'C0007131', 'C0007137', 'C0007138', 'C0020459', 'C0021670', 'C0026764', 'C0027819', 'C0030807', 'C0041341', 'C0334634', 'C1621958', 'C0007134', 'C0011849', 'C0020443', 'C0020474', 'C0242339', 'C0020473', 'C0008312', 'C0020557', 'C0028754', 'C0473527', 'C0005586', 'C0005587', 'C0011581', 'C0030319', 'C0036341', 'C0497327', 'C1269683', 'C2267227', 'C0028768', 'C0038436', 'C0600142', 'C0001973', 'C0009186', 'C0010414', 'C0917801', 'C0011615', 'C0033774', 'C0149922', 'C0033860', 'C0033975', 'C0012734', 'C0162565', 'C0009766', 'C0010200', 'C0018621', 'C0027497', 'C0030305', 'C0035455', 'C0042109', 'C0042963', 'C0040517', 'C0036337', 'C0003125', 'C0002395', 'C0004352', 'C0003950', 'C0086227', 'C0002994', 'C0037383', 'C2607914', 'C0014544', 'C0234974', 'C0494475', 'C0026769', 'C0003469', 'C0018801', 'C0270327', 'C0520909', 'C0233523', 'C0029408', 'C0003862', 'C0003864', 'C0006444', 'C0013390', 'C0015967', 'C0018099', 'C0030193', 'C0038013', 'C0038358', 'C0039503', 'C0040460', 'C0149931', 'C0184567', 'C0231528', 'C0393735', 'C3495559', 'C0001144', 'C0009443', 'C0036508', 'C0039103', 'C0040252', 'C0040259', 'C0003868', 'C2215257', 'C0004604', 'C0027051', 'C0032463', 'C0948089', 'C0013274', 'C0033893', 'C0030201', 'C0035435', 'C0025323', 'C0009324', 'C0022602', 'C0029878', 'C0034065', 'C0150055', 'C0011644', 'C0014175', 'C0019829', 'C0042164', 'C0013604', 'C0018802', 'C0030567', 'C0001403', 'C0002880', 'C0003872', 'C0004364', 'C0005138', 'C0006114', 'C0010032', 'C0010043', 'C0010346', 'C0011606', 'C0011608', 'C0011616', 'C0014742', 'C0015230', 'C0015397', 'C0017612', 'C0019112', 'C0021390', 'C0022073', 'C0022081', 'C0022104', 'C0022568', 'C0023052', 'C0024138', 'C0024141', 'C0024305', 'C0026857', 'C0026948', 'C0027726', 'C0028840', 'C0033246', 'C0035439', 'C0035854', 'C0036202', 'C0036830', 'C0038363', 'C0040147', 'C0079773', 'C0085074', 'C0242459', 'C1260899', 'C0001627', 'C0010674', 'C0018916', 'C0024301', 'C0026896', 'C0027873', 'C0035457', 'C0037274', 'C0041321', 'C0474368', 'C0524662', 'C0002390', 'C0004031', 'C0032533', 'C0034069', 'C0038325', 'C0039483', 'C0406486', 'C1527336', 'C1527407', 'C0011633', 'C0011991', 'C0013264', 'C0023434', 'C0024419', 'C0024530', 'C0079744', 'C0002171', 'C0006846', 'C0022548', 'C0023646', 'C0042900', 'C0002726', 'C0042165', 'C0242383', 'C0339293', 'C0037199', 'C0004238', 'C0011881', 'C0038454', 'C0020538', 'C0002962', 'C0002963', 'C0030590', 'C0008311', 'C0038220', 'C0014549', 'C0032897', 'C0014547', 'C0040997', 'C0154731', 'C0001206', 'C0020649', 'C0024586', 'C0242422', 'C0600177', 'C0015371', 'C0016053', 'C0002736', 'C0035258', 'C0596170', 'C0020514', 'C0017168', 'C0152020', 'C0027404', 'C0028043', 'C0085159', 'C0002874', 'C0023480', 'C0035335', 'C0278996', 'C1135868', 'C0018790', 'C0042510', 'C0007194', 'C0039240', 'C0700053', 'C0151636', 'C0017601', 'C0020542', 'C2973725', 'C0002170', 'C0026838', 'C1739363', 'C0006267', 'C0006277', 'C0020651', 'C0034067', 'C0917798', 'C0007129', 'C0036220', 'C0238122', 'C0009088', 'C0042376', 'C0014550', 'C0014553', 'C0270846', 'C0033845', 'C2239176', 'C0003962', 'C1261473', 'C0206682', 'C0238463', 'C0020428', 'C0206754', 'C0024623', 'C0025202', 'C0027796', 'C0020598', 'C0020626', 'C0029458', 'C0042870', 'C0035579', 'C0035086', 'C0085682', 'C3536984', 'C0039621', 'C1527383', 'C0011860', 'C0003969', 'C0013238', 'C0022575', 'C0524910', 'C0001261', 'C0003175', 'C0006309', 'C0018081', 'C0023860', 'C0031099', 'C0031350', 'C0032064', 'C0032285', 'C0034362', 'C0039128', 'C0042029', 'C0023269', 'C0023467', 'C0027708', 'C0035412', 'C0079774', 'C0149925', 'C0553580', 'C1140680', 'C0153594', 'C0004626', 'C0014347', 'C0029443', 'C0031154', 'C0032308', 'C0152936', 'C0155862', 'C0243001', 'C0524688', 'C0554628', 'C1318973', 'C0003850', 'C0020479', 'C0520679', 'C0751967', 'C0162836', 'C0221074', 'C0032768', 'C0019360', 'C0030783', 'C0002792', 'C0013404', 'C0043144', 'C0271623', 'C0021603', 'C0019345', 'C0010417', 'C0020635', 'C0022735', 'C0034012', 'C0948896', 'C0020615', 'C0032797', 'C0392164', 'C0011854', 'C0024535', 'C0024537', 'C0220756', 'C0018021', 'C0020676', 'C0027145', 'C0677607', 'C0409818', 'C2316212', 'C0007780', 'C0019080', 'C0040038', 'C0040053', 'C0149871', 'C0524702', 'C0016412', 'C0162316', 'C0751964', 'C0085096')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Driver function\n",
+    "lista = disease_new\n",
+    "print(convert(lista))\n",
+    "list_disease_new = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd=f\"\"\" \n",
+    "SELECT DISTINCT\n",
+    "    i.class_range,d.disease_id\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",
+    "    AND d.disease_id IN {list_disease_new};  \n",
+    "    \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd=pd.read_sql(get_icd, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_class = get_icd[\"class_range\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('L00-L99', 'A00-B99', 'E00-E90', 'E00-E90', 'F00-F99', 'L00-L99', 'L00-L99', 'J00-J99', 'G00-G99', 'E00-E90', 'G00-G99', 'D50-D89', 'I00-I99', 'I00-I99', 'S00-T98', 'F00-F99', 'A00-B99', 'F00-F99', 'I00-I99', 'M00-M99', 'M00-M99', 'L00-L99', 'M00-M99', 'R00-R99', 'E00-E90', 'A00-B99', 'J00-J99', 'I00-I99', 'F00-F99', 'M00-M99', 'M00-M99', 'J00-J99', 'J00-J99', 'F00-F99', 'G00-G99', 'C00-D48', 'J00-J99', 'J00-J99', 'A00-B99', 'C00-D48', 'M00-M99', 'C00-D48', 'C00-D48', 'I00-I99', 'K00-K93', 'K00-K93', 'G00-G99', 'A00-B99', 'K00-K93', 'J00-J99', 'H00-H59', 'R00-R99', 'K00-K93', 'A00-B99', 'Q00-Q99', 'E00-E90', 'F00-F99', 'L00-L99', 'L00-L99', 'L00-L99', 'L00-L99', 'M00-M99', 'E00-E90', 'E00-E90', 'E00-E90', 'R00-R99', 'F00-F99', 'H00-H59', 'Q00-Q99', 'N00-N99', 'R00-R99', 'R00-R99', 'N00-N99', 'G00-G99', 'L00-L99', 'R00-R99', 'H00-H59', 'R00-R99', 'M00-M99', 'E00-E90', 'K00-K93', 'H00-H59', 'H00-H59', 'E00-E90', 'A00-B99', 'M00-M99', 'J00-J99', 'I00-I99', 'I00-I99', 'I00-I99', 'C00-D48', 'R00-R99', 'K00-K93', 'A00-B99', 'A00-B99', 'C00-D48', 'E00-E90', 'E00-E90', 'E00-E90', 'E00-E90', 'E00-E90', 'I00-I99', 'I00-I99', 'E00-E90', 'E00-E90', 'E00-E90', 'E00-E90', 'I00-I99', 'I00-I99', 'E00-E90', 'H00-H59', 'K00-K93', 'L00-L99', 'H00-H59', 'L00-L99', 'Q00-Q99', 'J00-J99', 'C00-D48', 'C00-D48', 'C00-D48', 'L00-L99', 'A00-B99', 'L00-L99', 'M00-M99', 'C00-D48', 'C00-D48', 'C00-D48', 'A00-B99', 'A00-B99', 'A00-B99', 'E00-E90', 'C00-D48', 'N00-N99', 'C00-D48', 'G00-G99', 'G00-G99', 'C00-D48', 'I00-I99', 'E00-E90', 'G00-G99', 'R00-R99', 'N00-N99', 'G00-G99', 'F00-F99', 'E00-E90', 'F00-F99', 'H00-H59', 'M00-M99', 'M00-M99', 'M00-M99', 'H60-H95', 'R00-R99', 'G00-G99', 'K00-K93', 'F00-F99', 'G00-G99', 'L00-L99', 'K00-K93', 'K00-K93', 'A00-B99', 'J00-J99', 'C00-D48', 'M00-M99', 'Q00-Q99', 'K00-K93', 'L00-L99', 'G00-G99', 'L00-L99', 'G00-G99', 'E00-E90', 'I00-I99', 'J00-J99', 'A00-B99', 'N00-N99', 'G00-G99', 'M00-M99', 'I00-I99', 'J00-J99', 'E00-E90', 'L00-L99', 'D50-D89', 'C00-D48', 'F00-F99', 'F00-F99', 'L00-L99', 'S00-T98', 'J00-J99', 'R00-R99', 'M00-M99', 'L00-L99', 'K00-K93', 'F00-F99', 'I00-I99', 'A00-B99', 'I00-I99', 'M00-M99', 'R00-R99', 'E00-E90', 'A00-B99', 'K00-K93', 'F00-F99', 'G00-G99', 'A00-B99', 'Q00-Q99', 'N00-N99', 'L00-L99', 'H00-H59', 'I00-I99', 'E00-E90', 'L00-L99', 'R00-R99', 'R00-R99', 'C00-D48', 'C00-D48', 'L00-L99', 'I00-I99', 'I00-I99', 'L00-L99', 'G00-G99', 'I00-I99', 'K00-K93', 'A00-B99', 'C00-D48', 'J00-J99', 'D50-D89', 'L00-L99', 'R00-R99', 'C00-D48', 'E00-E90', 'F00-F99', 'M00-M99', 'C00-D48', 'N00-N99', 'F00-F99', 'C00-D48', 'H00-H59', 'J00-J99', 'N00-N99', 'E00-E90', 'C00-D48', 'C00-D48', 'M00-M99', 'E00-E90', 'G00-G99', 'F00-F99', 'G00-G99', 'F00-F99', 'A00-B99', 'A00-B99', 'F00-F99', 'E00-E90', 'I00-I99', 'I00-I99', 'G00-G99', 'C00-D48', 'D50-D89', 'F00-F99', 'F00-F99', 'A00-B99', 'M00-M99', 'J00-J99', 'N00-N99', 'C00-D48', 'F00-F99', 'M00-M99', 'J00-J99', 'M00-M99')\n"
+     ]
+    }
+   ],
+   "source": [
+    "  # Driver function\n",
+    "lista = get_icd_class\n",
+    "print(convert(lista))\n",
+    "class_range = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_name= f\"\"\"\n",
+    "SELECT *\n",
+    "FROM disnet_biolayer.tmp_icd\n",
+    "WHERE class_range in {class_range};\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_name=pd.read_sql(get_icd_name, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_disease_id = get_icd.merge(get_icd_name,how = \"inner\",on = \"class_range\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_disease_id = class_rang_name_disease_id.drop([\"class_range\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_disease_id = class_rang_name_disease_id.rename(columns={\"class_name\":\"class_name_ori\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_new = get_icd.merge(get_icd_name,how = \"inner\",on = \"class_range\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_new = class_rang_name_new.drop([\"class_range\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_new = class_rang_name_new.rename(columns={\"disease_id\": \"disease_new\",\"class_name\":\"class_name_new\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis = Triples_target_final.merge(class_rang_name_disease_id,on='disease_id').merge(class_rang_name_new,on='disease_new')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mental and behavioural disorders                                2111\n",
+       "Diseases of the nervous system                                  1043\n",
+       "Diseases of the musculoskeletal system and connective tissue    1033\n",
+       "Endocrine, nutritional and metabolic diseases                    671\n",
+       "Diseases of the circulatory system                               347\n",
+       "Neoplasms                                                         42\n",
+       "Name: class_name_ori, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.value_counts(final_df_type_dis['class_name_ori'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_cat = pd.value_counts(final_df_type_dis['class_name_new'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>class_name_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mental and behavioural disorders</th>\n",
+       "      <td>1719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the skin and subcutaneous tissue</th>\n",
+       "      <td>491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the nervous system</th>\n",
+       "      <td>473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the musculoskeletal system and connective tissue</th>\n",
+       "      <td>413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified</th>\n",
+       "      <td>365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the circulatory system</th>\n",
+       "      <td>282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the respiratory system</th>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Endocrine, nutritional and metabolic diseases</th>\n",
+       "      <td>264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Neoplasms</th>\n",
+       "      <td>263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the digestive system</th>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Certain infectious and parasitic diseases</th>\n",
+       "      <td>148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the eye and adnexa</th>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the genitourinary system</th>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Injury, poisoning and certain other consequences of external causes</th>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism</th>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Congenital malformations, deformations and chromosomal abnormalities</th>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diseases of the ear and mastoid process</th>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    class_name_new\n",
+       "Mental and behavioural disorders                              1719\n",
+       "Diseases of the skin and subcutaneous tissue                   491\n",
+       "Diseases of the nervous system                                 473\n",
+       "Diseases of the musculoskeletal system and conn...             413\n",
+       "Symptoms, signs and abnormal clinical and labor...             365\n",
+       "Diseases of the circulatory system                             282\n",
+       "Diseases of the respiratory system                             273\n",
+       "Endocrine, nutritional and metabolic diseases                  264\n",
+       "Neoplasms                                                      263\n",
+       "Diseases of the digestive system                               167\n",
+       "Certain infectious and parasitic diseases                      148\n",
+       "Diseases of the eye and adnexa                                 141\n",
+       "Diseases of the genitourinary system                            99\n",
+       "Injury, poisoning and certain other consequence...              58\n",
+       "Diseases of the blood and blood-forming organs ...              55\n",
+       "Congenital malformations, deformations and chro...              20\n",
+       "Diseases of the ear and mastoid process                         16"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(new_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HEATMAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis_heatmap = final_df_type_dis.drop([\"disease_id\",\"drug_id\",\"score\",\"gene_id\",\"disease_new\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis_heatmap[\"frecuency\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#final_df_type_dis_heatmap.to_excel(\"heatmap_target_final_triples.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = pd.read_excel(\"./Data/Input/DISNET/heatmap_target_final_triples.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = group_dis.groupby(['class_name_ori',\"class_name_new\"])[\"frecuency\"].sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "x_array = np.array(group_dis[\"frecuency\"])\n",
+    "normalized_arr = preprocessing.normalize([x_array])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_arr = pd.DataFrame(normalized_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_arr = normalized_arr.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis[\"Norm_fre\"] = normalized_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = group_dis.pivot(index='class_name_new', columns='class_name_ori', values='Norm_fre')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#final_df_type_dis_heatmap = final_df_type_dis_heatmap.pivot(\"class_name_pwb\", \"class_name_nopwb\")\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax = sns.heatmap(group_dis,cmap=sns.cubehelix_palette(as_cmap=True),vmin=0, vmax=1)#PiYG\n",
+    "#plt.title('Heatmap of type of diseases', fontsize = 15) # title with fontsize 20\n",
+    "plt.xlabel('Target gene - based treated diseases', fontsize = 10) # x-axis label with fontsize 15\n",
+    "plt.ylabel('Target gene - based treated diseases', fontsize = 10) # y-axis label with fontsize 15\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"Heatmap_target.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
+}
--- a/Notebooks/Results/.ipynb_checkpoints/4.2 Types of diseases - DREBIOP-checkpoint.ipynb
+++ b/Notebooks/Results/.ipynb_checkpoints/4.2 Types of diseases - DREBIOP-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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 sqlalchemy import create_engine\n",
+    "from sklearn import preprocessing\n",
+    "import mysql.connector\n",
+    "from pandas import DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_triplets_repodb_union = pd.read_csv(\"./Data/Input/DISNET/all_triplets_repodb_union.tsv\", sep='\\t')\n",
+    "all_triplets_repodb_union = all_triplets_repodb_union.drop([\"Unnamed: 0\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_triplets_repodb_union_ = all_triplets_repodb_union.drop([\"pathway_id\",\"gene_id\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diseases_PwB = all_triplets_repodb_union[\"disease_PwB\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diseases_PwB = diseases_PwB.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diseases_no_PwB = all_triplets_repodb_union[\"disease_no_PwB\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diseases_no_PwB = diseases_no_PwB.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C0018802', 'C0004238', 'C0011881', 'C0001206', 'C0020649', 'C0024586', 'C0010674', 'C0003962', 'C0013604', 'C0020428', 'C0020473', 'C0018801', 'C0004096', 'C0009324', 'C0010346', 'C0011849', 'C0020598', 'C0020626', 'C0024141', 'C0026769', 'C0028754', 'C0029456', 'C0029458', 'C0015371', 'C0035086', 'C0002395', 'C0003862', 'C0003873', 'C0004153', 'C0004604', 'C0009443', 'C0026764', 'C0001144', 'C0001261', 'C0003175', 'C0006277', 'C0006309', 'C0011581', 'C0018081', 'C0023860', 'C0027404', 'C0030193', 'C0016053', 'C0184567', 'C0242422', 'C0600177', 'C1621958', 'C0027051', 'C0038454', 'C0036337', 'C0036341', 'C0042870', 'C1739363', 'C0042847', 'C0162316', 'C0006114', 'C0033860', 'C0085682', 'C0030567', 'C0497327', 'C3536984', 'C0039621', 'C1527383', 'C0029408', 'C0032463', 'C0040460', 'C0149931', 'C0393735', 'C0948089', 'C0031099', 'C0031350', 'C0032064', 'C0032285', 'C0034362', 'C0035854', 'C0037199', 'C0039128', 'C0042029', 'C1269683')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Driver function\n",
+    "lista = diseases_no_PwB\n",
+    "print(convert(lista))\n",
+    "list_dis_no_pwb = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('C0020538', 'C0030567', 'C0035579', 'C0026769', 'C0002892', 'C0002395', 'C0025202')\n"
+     ]
+    }
+   ],
+   "source": [
+    "def convert(list):\n",
+    "    return tuple(i for i in list)\n",
+    "  \n",
+    "# Driver function\n",
+    "lista = diseases_PwB\n",
+    "print(convert(lista))\n",
+    "list_dis_pwb = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd=f\"\"\" \n",
+    "SELECT DISTINCT\n",
+    "    i.class_range,d.disease_id\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",
+    "    AND d.disease_id IN {list_dis_pwb};  \n",
+    "    \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd=pd.read_sql(get_icd, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_class = get_icd[\"class_range\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('D50-D89', 'I00-I99', 'G00-G99', 'G00-G99', 'G00-G99', 'E00-E90')\n"
+     ]
+    }
+   ],
+   "source": [
+    "  # Driver function\n",
+    "lista = get_icd_class\n",
+    "print(convert(lista))\n",
+    "class_range = convert(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_name= f\"\"\"\n",
+    "SELECT *\n",
+    "FROM disnet_biolayer.tmp_icd\n",
+    "WHERE class_range in {class_range};\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_icd_name=pd.read_sql(get_icd_name, con=disnet_db_ares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_nopwb = get_icd.merge(get_icd_name,how = \"inner\",on = \"class_range\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_nopwb_fil = class_rang_name_nopwb.drop([\"class_range\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_nopwb_fil = class_rang_name_nopwb_fil.rename(columns={\"disease_id\": \"disease_no_PwB\",\"class_name\":\"class_name_nopwb\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_pwb = get_icd.merge(get_icd_name,how = \"inner\",on = \"class_range\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_pwb_fil = class_rang_name_pwb.drop([\"class_range\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_rang_name_pwb_fil = class_rang_name_pwb_fil.rename(columns={\"disease_id\": \"disease_PwB\",\"class_name\":\"class_name_pwb\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis = all_triplets_repodb_union.merge(class_rang_name_pwb_fil,on='disease_PwB').merge(class_rang_name_nopwb_fil,on='disease_no_PwB')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Diseases of the nervous system                   4\n",
+       "Endocrine, nutritional and metabolic diseases    1\n",
+       "Name: class_name_pwb, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.value_counts(final_df_type_dis['class_name_pwb'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_cat = pd.value_counts(final_df_type_dis['class_name_nopwb'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HEATMAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis_heatmap = final_df_type_dis.drop([\"disease_PwB\",\"drug_id\",\"pathway_id\",\"gene_id\",\"disease_no_PwB\"],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df_type_dis_heatmap[\"frecuency\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = pd.read_excel(\"./Data/Input/DISNET/Type_dis_drebiop.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = group_dis.groupby(['class_name_pwb',\"class_name_nopwb\"])[\"frecuency\"].sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.03054236 0.06108472 0.03054236 0.03054236 0.42759306 0.06108472\n",
+      "  0.03054236 0.03054236 0.06108472 0.09162708 0.06108472 0.03054236\n",
+      "  0.24433889 0.18325417 0.06108472 0.30542361 0.24433889 0.21379653\n",
+      "  0.15271181 0.12216944 0.06108472 0.39705069 0.03054236 0.18325417\n",
+      "  0.24433889 0.42759306 0.03054236 0.12216944]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "x_array = np.array(group_dis[\"frecuency\"])\n",
+    "normalized_arr = preprocessing.normalize([x_array])\n",
+    "print(normalized_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_arr = pd.DataFrame(normalized_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_arr = normalized_arr.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis[\"Norm_fre\"] = normalized_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_dis = group_dis.pivot(index='class_name_nopwb', columns='class_name_pwb', values='Norm_fre')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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>class_name_pwb</th>\n",
+       "      <th>D50-D89 (Immune mechanism)</th>\n",
+       "      <th>E00-E90 (Metabolic)</th>\n",
+       "      <th>G00-G99 (Nervous)</th>\n",
+       "      <th>I00-I99 (Circulatory)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class_name_nopwb</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A00-B99 (Infectious)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C00-D48 (Neoplasms)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.183254</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D50-D89 (Immune mechanism)</th>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E00-E90 (Metabolic)</th>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>0.427593</td>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>0.244339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F00-F99 (Mental)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>0.305424</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G00-G99 (Nervous)</th>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>0.244339</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I00-I99 (Circulatory)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.213797</td>\n",
+       "      <td>0.427593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J00-J99 (Respiratory)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>0.152712</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K00-K93 (Digestive)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>0.122169</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L00-L99 (Skin)</th>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M00-M99 (Musculoskeletal)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.091627</td>\n",
+       "      <td>0.397051</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N00-N99 (Genitourinary)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.061085</td>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>0.030542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R00-R99 (Symptoms)</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.030542</td>\n",
+       "      <td>0.183254</td>\n",
+       "      <td>0.122169</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "class_name_pwb              D50-D89 (Immune mechanism)  E00-E90 (Metabolic)  \\\n",
+       "class_name_nopwb                                                              \n",
+       "A00-B99 (Infectious)                               NaN                  NaN   \n",
+       "C00-D48 (Neoplasms)                                NaN                  NaN   \n",
+       "D50-D89 (Immune mechanism)                    0.030542                  NaN   \n",
+       "E00-E90 (Metabolic)                           0.061085             0.427593   \n",
+       "F00-F99 (Mental)                                   NaN             0.061085   \n",
+       "G00-G99 (Nervous)                             0.030542             0.030542   \n",
+       "I00-I99 (Circulatory)                              NaN                  NaN   \n",
+       "J00-J99 (Respiratory)                              NaN             0.030542   \n",
+       "K00-K93 (Digestive)                                NaN             0.061085   \n",
+       "L00-L99 (Skin)                                0.030542                  NaN   \n",
+       "M00-M99 (Musculoskeletal)                          NaN             0.091627   \n",
+       "N00-N99 (Genitourinary)                            NaN             0.061085   \n",
+       "R00-R99 (Symptoms)                                 NaN             0.030542   \n",
+       "\n",
+       "class_name_pwb              G00-G99 (Nervous)  I00-I99 (Circulatory)  \n",
+       "class_name_nopwb                                                      \n",
+       "A00-B99 (Infectious)                 0.244339                    NaN  \n",
+       "C00-D48 (Neoplasms)                  0.183254                    NaN  \n",
+       "D50-D89 (Immune mechanism)                NaN                    NaN  \n",
+       "E00-E90 (Metabolic)                  0.061085               0.244339  \n",
+       "F00-F99 (Mental)                     0.305424                    NaN  \n",
+       "G00-G99 (Nervous)                    0.244339                    NaN  \n",
+       "I00-I99 (Circulatory)                0.213797               0.427593  \n",
+       "J00-J99 (Respiratory)                0.152712                    NaN  \n",
+       "K00-K93 (Digestive)                  0.122169                    NaN  \n",
+       "L00-L99 (Skin)                       0.061085                    NaN  \n",
+       "M00-M99 (Musculoskeletal)            0.397051                    NaN  \n",
+       "N00-N99 (Genitourinary)              0.030542               0.030542  \n",
+       "R00-R99 (Symptoms)                   0.183254               0.122169  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "group_dis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax = sns.heatmap(group_dis,cmap=sns.cubehelix_palette(as_cmap=True),vmin=0, vmax=1)#PiYG\n",
+    "plt.xlabel('Pathway - based treated diseases', fontsize = 10) # x-axis label with fontsize 15\n",
+    "plt.ylabel('Target gene - based treated diseases', fontsize = 10) # y-axis label with fontsize 15\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"Heatmap_pws.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
+}
--- a/Notebooks/Results/1.1 Patterns DREBIOP - Pathway-based treated disease.ipynb
+++ b/Notebooks/Results/1.1 Patterns DREBIOP - Pathway-based treated disease.ipynb
@@ -29,7 +29,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "Triples_target_final = pd.read_csv(\"Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -39,7 +39,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "triplets_total = pd.read_csv('triplets_total.csv', sep=';')"
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
   ]
  },
  {
@@ -288,120 +288,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "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>pathway_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>C0020538</td>\n",
-       "      <td>WP554</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>C0018799</td>\n",
-       "      <td>WP1544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>C0018799</td>\n",
-       "      <td>WP1528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>C0027947</td>\n",
-       "      <td>WP229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>C0013369</td>\n",
-       "      <td>WP229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>659</th>\n",
-       "      <td>C0268274</td>\n",
-       "      <td>WP4153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>660</th>\n",
-       "      <td>C0085131</td>\n",
-       "      <td>WP4153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>661</th>\n",
-       "      <td>C0036161</td>\n",
-       "      <td>WP4153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>662</th>\n",
-       "      <td>C0268275</td>\n",
-       "      <td>WP4153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>663</th>\n",
-       "      <td>C0162666</td>\n",
-       "      <td>WP4236</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>664 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    disease_id pathway_id\n",
-       "0     C0020538      WP554\n",
-       "1     C0018799     WP1544\n",
-       "2     C0018799     WP1528\n",
-       "3     C0027947      WP229\n",
-       "4     C0013369      WP229\n",
-       "..         ...        ...\n",
-       "659   C0268274     WP4153\n",
-       "660   C0085131     WP4153\n",
-       "661   C0036161     WP4153\n",
-       "662   C0268275     WP4153\n",
-       "663   C0162666     WP4236\n",
-       "\n",
-       "[664 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
   "source": [
-    "dis_path_direct = pd.read_csv('disease_pathway.tsv', sep='\\t')\n",
+    "dis_path_direct = pd.read_csv('./Data/Input/DISNET/disease_pathway.tsv', sep='\\t')"
-    "dis_path_direct"
   ]
  },
  {

--- a/Notebooks/Results/1.2 Patterns DREBIOP - Target gene-based treated disease.ipynb
+++ b/Notebooks/Results/1.2 Patterns DREBIOP - Target gene-based treated disease.ipynb
@@ -29,7 +29,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "Triples_target_final = pd.read_csv(\"Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -39,7 +39,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "triplets_total = pd.read_csv('triplets_total.csv', sep=';')"
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
   ]
  },
  {
@@ -264,7 +264,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "dis_path_direct = pd.read_csv('disease_pathway.tsv', sep='\\t')"
+    "dis_path_direct = pd.read_csv('./Data/Input/DISNET/disease_pathway.tsv', sep='\\t')"
   ]
  },
  {

--- a/Notebooks/Results/2. GDAs pathways DREGE-DREBIOP-DISNET.ipynb
+++ b/Notebooks/Results/2. GDAs pathways DREGE-DREBIOP-DISNET.ipynb
@@ -29,7 +29,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "dis_gen = pd.read_csv('dis_genes.tsv', sep='\\t')\n",
+    "dis_gen = pd.read_csv('./Data/Input/DISNET/dis_genes.tsv', sep='\\t')\n",
    "dis_gen = dis_gen.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -112,7 +112,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "cases_repodb_target = pd.read_csv(\"score_gdas_repodb_target_final.tsv\", sep='\\t')\n",
+    "cases_repodb_target = pd.read_csv(\"./Data/Input/DISNET/score_gdas_repodb_target_final.tsv\", sep='\\t')\n",
    "cases_repodb_target = cases_repodb_target.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -323,7 +323,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "score_gdas_two_repodb = pd.read_csv('score_gdas_two_repodb.tsv', sep='\\t')\n",
+    "score_gdas_two_repodb = pd.read_csv('./Data/Input/DISNET/score_gdas_two_repodb.tsv', sep='\\t')\n",
    "score_gdas_two_repodb = score_gdas_two_repodb.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -333,7 +333,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "score_gdas_one_repodb = pd.read_csv('score_gdas_one_repodb.tsv', sep='\\t')\n",
+    "score_gdas_one_repodb = pd.read_csv('./Data/Input/DISNET/score_gdas_one_repodb.tsv', sep='\\t')\n",
    "score_gdas_one_repodb = score_gdas_one_repodb.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -379,7 +379,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "cases_csbj_target = pd.read_csv('score_gdas_csbj_target_filtergen.tsv', sep='\\t')\n",
+    "cases_csbj_target = pd.read_csv('./Data/Input/DISNET/score_gdas_csbj_target_filtergen.tsv', sep='\\t')\n",
    "cases_csbj_target = cases_csbj_target.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -427,7 +427,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "score_gdas_csbj = pd.read_csv('score_gdas_csbj.tsv', sep='\\t')\n",
+    "score_gdas_csbj = pd.read_csv('./Data/Input/DISNET/score_gdas_csbj.tsv', sep='\\t')\n",
    "score_gdas_csbj = score_gdas_csbj.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -564,7 +564,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "all_gdas_target_.to_csv(\"Triples_target_final.tsv\", sep='\\t')"
+    "all_gdas_target_.to_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')"
   ]
  },
  {

--- a/Notebooks/Results/3.1 Type of drugs - MESHPA.ipynb
+++ b/Notebooks/Results/3.1 Type of drugs - MESHPA.ipynb
@@ -20,7 +20,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "Triples_target_final = pd.read_csv(\"Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -68,7 +68,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "triplets_total = pd.read_csv('triplets_total.csv', sep=';')"
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
   ]
  },
  {
@@ -113,7 +113,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "type_drug =pd.read_excel(\"Drug_Categories.xlsx\", engine='openpyxl')"
+    "type_drug =pd.read_excel(\"./Data/Input/DISNET/Drug_Categories.xlsx\", engine='openpyxl')"
   ]
  },
  {

--- a/Notebooks/Results/3.2 Types of drugs - ATC.ipynb
+++ b/Notebooks/Results/3.2 Types of drugs - ATC.ipynb
@@ -20,7 +20,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "Triples_target_final = pd.read_csv(\"Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -48,7 +48,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "triplets_total = pd.read_csv('triplets_total.csv', sep=';')"
+    "triplets_total = pd.read_csv('./Data/Input/DISNET/triplets_total.csv', sep=';')"
   ]
  },
  {
@@ -84,7 +84,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "drug_atc = pd.read_csv('drug_atc.tsv', sep='\\t')\n",
+    "drug_atc = pd.read_csv('./Data/Input/DISNET/drug_atc.tsv', sep='\\t')\n",
    "drug_atc = drug_atc.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -202,7 +202,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "atc_name = pd.read_excel(\"ATC_desc_name.xlsx\")\n",
+    "atc_name = pd.read_excel(\"./Data/Input/DISNET/ATC_desc_name.xlsx\")\n",
    "atc_name['index'] = atc_name['index'].str.strip()"
   ]
  },

--- a/Notebooks/Results/4.1 Types of diseases - DREGE.ipynb
+++ b/Notebooks/Results/4.1 Types of diseases - DREGE.ipynb
@@ -22,7 +22,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "Triples_target_final = pd.read_csv(\"Triples_target_final.tsv\", sep='\\t')\n",
+    "Triples_target_final = pd.read_csv(\"./Data/Input/DISNET/Triples_target_final.tsv\", sep='\\t')\n",
    "Triples_target_final = Triples_target_final.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -198,11 +198,9 @@
   "outputs": [],
   "source": [
    "get_icd_name= f\"\"\"\n",
-    "SELECT\n",
+    "SELECT *\n",
-    "*\n",
    "FROM disnet_biolayer.tmp_icd\n",
-    "WHERE\n",
+    "WHERE class_range in {class_range};\"\"\""
-    "class_range in {class_range};\"\"\""
   ]
  },
  {
@@ -484,7 +482,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "group_dis = pd.read_excel(\"heatmap_target_final_triples.xlsx\")"
+    "group_dis = pd.read_excel(\"./Data/Input/DISNET/heatmap_target_final_triples.xlsx\")"
   ]
  },
  {

--- a/Notebooks/Results/4.2 Types of diseases - DREBIOP.ipynb
+++ b/Notebooks/Results/4.2 Types of diseases - DREBIOP.ipynb
@@ -22,7 +22,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "all_triplets_repodb_union = pd.read_csv(\"all_triplets_repodb_union.tsv\", sep='\\t')\n",
+    "all_triplets_repodb_union = pd.read_csv(\"./Data/Input/DISNET/all_triplets_repodb_union.tsv\", sep='\\t')\n",
    "all_triplets_repodb_union = all_triplets_repodb_union.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
@@ -182,11 +182,9 @@
   "outputs": [],
   "source": [
    "get_icd_name= f\"\"\"\n",
-    "SELECT\n",
+    "SELECT *\n",
-    "*\n",
    "FROM disnet_biolayer.tmp_icd\n",
-    "WHERE\n",
+    "WHERE class_range in {class_range};\"\"\""
-    "class_range in {class_range};\"\"\""
   ]
  },
  {
@@ -323,7 +321,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "group_dis = pd.read_excel(\"Type_dis_drebiop.xlsx\")"
+    "group_dis = pd.read_excel(\"./Data/Input/DISNET/Type_dis_drebiop.xlsx\")"
   ]
  },
  {