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create_inferred_lookups.py
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import requests
import json
import pandas as pd
from datetime import date
import time
import os
from grouping_atc import atc_grouping_third, atc_grouping_second
from sugeno_functions import compute_sugeno
import numpy as np
def grouping(mergedResponse, grouping="ATC"):
if grouping=="ATC":
df = pd.read_csv('ATC level 3.csv')
df2 = pd.read_csv('ATC level 2.csv')
df_infores = pd.read_csv('InfoRes_Catalog.csv')
results = mergedResponse['fields']['data']['message']['results']
edges = mergedResponse['fields']['data']['message']['knowledge_graph']['edges']
nodes = mergedResponse['fields']['data']['message']['knowledge_graph']['nodes']
lookup_res = []
inferred_res = []
inferred_res_kl = []
for idi, i in enumerate(range(df_infores.shape[0])):
if df_infores.iloc[idi, 1] == "curated":
lookup_res.append(df_infores.iloc[idi, 0])
else:
inferred_res.append(df_infores.iloc[idi, 0])
inferred_res_kl.append(df_infores.iloc[idi, 1])
lookup = []
inferred = []
full = []
for idres, res in enumerate(results):
sugeno = round(res['sugeno'],2)
comp_novelty = round(res['ordering_components']['novelty'], 2)
comp_confidence = round(res['ordering_components']['confidence'], 2)
comp_clinical_evidence = round(res['ordering_components']['clinical_evidence'], 2)
# ara_scores = [[anal['score'], anal['resource_id']] for anal in res['analyses']]
improving_agent = False
improving_agent_score = -.0001
ARAX = False
ARAX_score = -.0001
unsecret = False
unsecret_score = -.0001
biothings_explorer = False
biothings_explorer_score = -.0001
aragorn = False
aragorn_score = -.0001
for analysis in res['analyses']:
if analysis['resource_id'] == "infores:improving-agent":
improving_agent = True
improving_agent_score = analysis['score']
elif analysis['resource_id'] == "infores:rtx-kg2":
ARAX = True
ARAX_score = analysis['score']
elif analysis['resource_id'] == "infores:biothings-explorer":
biothings_explorer = True
biothings_explorer_score = analysis['score']
elif analysis['resource_id'] == "infores:unsecret-agent":
unsecret = True
unsecret_score = analysis['score']
elif analysis['resource_id'] == "infores:aragorn":
aragorn = True
aragorn_score = analysis['score']
sugeno_conf_clin = round(compute_sugeno(score_confidence= comp_confidence, score_novelty=comp_novelty,
score_clinical=comp_clinical_evidence, weight_novelty=0.0)[2], 2)
sugeno_conf_clin_1_5 = round(compute_sugeno(score_confidence=comp_confidence, score_novelty=comp_novelty,
score_clinical=comp_clinical_evidence, weight_novelty=0.0,
weight_confidence=1.0, weight_clinical=0.5)[2], 2)
sugeno_conf_clin_5_1 = round(compute_sugeno(score_confidence=comp_confidence, score_novelty=comp_novelty,
score_clinical=comp_clinical_evidence, weight_novelty=0.0,
weight_confidence=0.5, weight_clinical=1.0)[2], 2)
sugeno_conf_clin_5_5 = round(compute_sugeno(score_confidence=comp_confidence, score_novelty=comp_novelty,
score_clinical=comp_clinical_evidence, weight_novelty=0.0,
weight_confidence=0.8, weight_clinical=0.8)[2], 2)
for anal in res['analyses']:
is_lookup = 0
edge_id = anal['edge_bindings']['t_edge'][0]['id']
prim_src_found = 0
for t in edges[edge_id]['sources']:
if t['resource_role'] == 'primary_knowledge_source':
if t['resource_id'] not in inferred_res:
is_lookup = 1
knowledge_level = "curated"
else:
knowledge_level = inferred_res_kl[inferred_res.index(t['resource_id'])]
prim_source = t['resource_id']
prim_src_found = 1
if prim_src_found ==0:
prim_source = "Not Found"
knowledge_level = "Not Found"
obj = edges[edge_id]['object']
subj = edges[edge_id]['subject']
if 'MONDO' in obj:
node = subj
else:
node = obj
if 'name' in nodes[node].keys():
node_name = nodes[node]['name']
else:
node_name = "NO NAME GIVEN"
found=0
if nodes[node]['attributes'] != [] :
for idi, i in enumerate(nodes[node]['attributes']):
if i['attribute_type_id'] == 'biothings_annotations':
found = 1
break
if found ==1:
if 'chembl' in nodes[node]['attributes'][idi]['value'][0].keys():
if isinstance(nodes[node]['attributes'][idi]['value'][0]['chembl'], dict):
if 'atc_classifications' in nodes[node]['attributes'][idi]['value'][0]['chembl'].keys():
atc_code = nodes[node]['attributes'][idi]['value'][0]['chembl']['atc_classifications']
else:
atc_code = "N/A"
elif isinstance(nodes[node]['attributes'][idi]['value'][0]['chembl'], list):
atc_found = 0
atc_code = []
for j in nodes[node]['attributes'][idi]['value'][0]['chembl']:
if 'atc_classifications' in j.keys():
atc_code.append(j['atc_classifications'])
atc_found = 1
if atc_found==0:
atc_code = "N/A"
else:
atc_code = "N/A"
else:
atc_code = "N/A"
else:
atc_code = "N/A"
if isinstance(atc_code, str):
atc_3 = ""
atc_3_name = ""
atc_3 = ''.join(atc_code.split())[:-3].upper()
atc_2 = ""
atc_2_name = ""
atc_2 = ''.join(atc_code.split())[:-4].upper()
if atc_3 !="":
atc_3_name = df.loc[df['level3']==atc_3]['level3_description'].values[0]
else:
atc_3 = "N/A"
atc_3_name = "N/A"
if atc_2 != "":
atc_2_name = df2.loc[df2['level2'] == atc_2]['level2_description'].values[0]
else:
atc_2 = "N/A"
atc_2_name = "N/A"
else:
atc_3, atc_3_name = [], []
for i in atc_code:
atc_3_str = ''.join(i.split())[:-3].upper()
atc_3.append(atc_3_str)
atc_3_name.append(df.loc[df['level3']==atc_3_str]['level3_description'].values[0])
atc_2, atc_2_name = [], []
for i in atc_code:
atc_2_str = ''.join(i.split())[:-4].upper()
atc_2.append(atc_2_str)
atc_2_name.append(df2.loc[df2['level2'] == atc_2_str]['level2_description'].values[0])
row = {
'Result_Num': idres,
'Drug_ID': node,
'Drug_Name': node_name,
'ATC_Code': atc_code,
'ATC_Code_3rd_Level': atc_3,
'ATC_Code_3rd_Level_Names': atc_3_name,
'sugeno_score': sugeno,
'comp_confidence_score': comp_confidence,
'comp_novelty_score': comp_novelty,
'comp_clinical_evidence_score': comp_clinical_evidence,
'ATC_Code_2nd_Level': atc_2,
'ATC_Code_2nd_Level_Names': atc_2_name,
'Knowledge Level': knowledge_level,
'Rank': res['rank'],
'Sugeno_Conf_Clin': sugeno_conf_clin,
'Sugeno_Conf_Clin_1_5': sugeno_conf_clin_1_5,
'Sugeno_Conf_Clin_5_1': sugeno_conf_clin_5_1,
'Sugeno_Conf_Clin_5_5': sugeno_conf_clin_5_5,
'ARAX': ARAX,
'ARAX_score': ARAX_score,
'unsecret': unsecret,
'unsecret_score': unsecret_score,
'improving_agent': improving_agent,
'improving_agent_score': improving_agent_score,
'biothings_explorer': biothings_explorer,
'biothings_explorer_score': biothings_explorer_score,
'aragorn': aragorn,
'aragorn_score': aragorn_score,
}
if is_lookup == 1:
if row not in lookup:
lookup.append(row)
else:
if row not in inferred:
inferred.append(row)
full.append(row)
inferred_not_repeated= []
for i in inferred:
ffound=0
for j in lookup:
if list(i.values())[:-6] == list(j.values())[:-6]:
ffound=1
break
if ffound==0:
inferred_not_repeated.append(i)
return lookup, inferred_not_repeated, full
disease_dict = {
"MONDO:0005267": "Heart Disorder",
# # "MONDO:0005709": "Common_Cold",
# "MONDO:0011786": "Allergic Rhinitis",
# # "MONDO:0005277": "Migraine Disorder",
# # "MONDO:0005148": "Type 2 Diabetes",
"MONDO:0004975": "Alzheimer Disease",
# # "MONDO:0005100": "Systemic Sclerosis",
# # "MONDO:0800044": "NGLY1-Deficiency",
"MONDO:0016063": "Cowden Disease",
"MONDO:0017314": "Ehlers-Danlos Disease",
"MONDO:0010808": "Familial Insomnia",
"MONDO:0018150": "Gaucher Disease",
"MONDO:0020333": "Aggressive Systemic Mastocytosis",
# # "MONDO:0000004": "Adrenocortical Hypofunction",
# # "MONDO:0005002": "Chronic Obstructive Pulmonary Disease",
# # "MONDO:0006936": "Pulmonary Stenosis",
"MONDO:0011426": "Aceruloplasminemia",
"MONDO:0007743": "attention deficit-hyperactivity disorder",
"MONDO:0004784": "Allergic Asthma",
"MONDO:0001505": "Alcoholic Hepatitis",
# "MONDO:0014252": "Approved for Familial defective apolipoprotein B (APOB)",
"MONDO:0007750": "Approved for Familial hypercholesterolemia heterozygous (LDLR)",
"MONDO:0005260": "Autism",
"MONDO:0015564": "Castleman Disease",
"MONDO:0006497": "Cerebral palsy",
"MONDO:0043233": "Exfoliative Dermatitis",
"MONDO:0008251": "familial pityriasis rubra pilaris",
"MONDO:0007186": "Gastroesophageal Reflux Disease",
"MONDO:0005393": "Gout",
"MONDO:0015364": "Hereditary Sensory And Autonomic Neuropathy Type",
"MONDO:0009746": "Hereditary sensory and autonomic neuropathy type 4",
"MONDO:0005799": "Hookworm Infectious Disease",
"MONDO:0100345": "Lactose intolerance",
"MONDO:0018493": "Malignant Hyperthermia Of Anesthesia",
"MONDO:0005301": "Multiple Sclerosis",
"MONDO:0018911": "Maturity-onset Diabetes Of The Young",
"MONDO:0010794": "NARP Syndrome",
"MONDO:0018958": "Nemaline myopathy",
"MONDO:0011873": "Niemann-Pick disease, type C",
"MONDO:0007147": "Obstructive Sleep Apnea",
"MONDO:0004260": "Peptic Ulcer Perforation",
"MONDO:0001119": "Premature Menopause",
"MONDO:0019173": "Rabies",
"MONDO:0004758": "scotoma",
"MONDO:0019600": "Xeroderma Pigmentosum",
"UMLS:C1689985": "Absence",
"UMLS:C1961102": "Acute Lymphoblastic Leukemia",
"UMLS:C0948216": "Adenocarcinoma of the Ovary",
"UMLS:C5204526": "Advanced Adenocarcinoma",
"UMLS:C0152069": "Alveolar echinococcosis",
"UMLS:C0268407": "Amyloid cardiomyopathy",
"UMLS:C0002726": "Amyloidosis",
"UMLS:C0393911": "Autonomic failure",
"UMLS:C0259749": "Autonomic neuropathy",
"UMLS:C0007097": "Carcinomas",
"UMLS:C1384641": "Cervical spondylosis",
"UMLS:C0346421": "Chronic eosinophilic leukemia",
"UMLS:C0009566": "Complication",
"UMLS:C0010043": "corneal ulcers",
"UMLS:C4551761": "Excessive daytime sleepiness",
"UMLS:C0031069": "Familial Mediterranean Fever",
"UMLS:C1456270": "fatty acid oxidation disorder",
"UMLS:C0037054": "Hemoglobin S",
"UMLS:C1328504": "Hormone refractory prostate cancer",
"UMLS:C0205725": "Human cytomegalovirus",
"UMLS:C0033300": "Hutchinson-Gilford Progeria Syndrome",
"UMLS:C4553297": "Hydatid Disease",
"UMLS:C1291314": "hydroxylase deficiency",
"UMLS:C0020598": "Hypocalcemia",
"UMLS:C0341862": "Hypothalamic amenorrhea",
"UMLS:C0206141": "Idiopathic Hypereosinophilic Syndrome",
"UMLS:C0024302": "large cell lymphoma",
"UMLS:C0149921": "lead poisoning in children",
"UMLS:C3554225": "LEPTIN RECEPTOR DEFICIENCY",
"UMLS:C0023869": "Lithiasis",
"UMLS:C0024523": "Malabsorption",
"UMLS:C1384494": "Metastatic Carcinoma",
"UMLS:C3538749": "Myeloma",
"UMLS:C0854467": "Myelosuppression",
"UMLS:C0027831": "Neurofibromatosis type 1",
"UMLS:C0855139": "Nodal Marginal Zone Lymphoma",
"UMLS:C1829844": "Organic acidemias",
"UMLS:C0268130": "OROTIC ACIDURIA",
"UMLS:C0030343": "Panuveitis",
"UMLS:C0687150": "Parathyroid Carcinoma",
"UMLS:C0039106": "Pigmented villonodular synovitis",
"UMLS:C0278791": "Refractory CLL",
"UMLS:C0085298": "SCD",
"UMLS:C1959629": "Seizure",
"UMLS:C0037933": "Spinal Diseases",
"UMLS:C0442893": "Systemic disease",
"UMLS:C0221013": "Systemic mastocytosis",
"UMLS:C0278851": "Thyroid carcinoma",
"UMLS:C4325554": "Traumatic anterior uveitis",
"UMLS:C0040954": "whipworm"
}
for tmp_str in ['CI', 'TEST']:
if tmp_str=='CI':
link = f"https://ars.ci.transltr.io/ars/api"
else:
link = f"https://ars.test.transltr.io/ars/api"
row_rank_list = []
for id_dis in disease_dict.keys():
done=0
name_dis = disease_dict[id_dis]
submit_filename = f"{name_dis}_Submit_{date.today()}_{tmp_str}.json"
if submit_filename not in os.listdir():
query = {
"message": {
"query_graph": {
"edges": {
"t_edge": {
"object": "on",
"subject": "sn",
"predicates": ["biolink:treats"]
}
},
"nodes": {
"on": {
"ids": [id_dis],
"categories": [
"biolink:Disease"
]
},
"sn": {
"categories": [
"biolink:ChemicalEntity"
]
}
}
}
}
}
response = requests.post(link + f"/submit", json=query).json()
with open(submit_filename, 'w') as f:
json.dump(response, f)
while True:
with open(submit_filename, 'r') as f:
id = json.load(f)['pk']
response = requests.get(link + f"/messages/{id}?trace=y")
data = response.json()
print(data['status'])
if data['status'] == 'Done' and data['merged_version'] != "None":
merged_version = data['merged_version']
print(f"For Disease {name_dis}, pk:{merged_version}")
url_merged_version = link + f'/messages/{merged_version}'
while True:
mergedResponse = requests.get(url_merged_version).json()
if mergedResponse['fields']['code'] == 200:
f_out = open(f"{name_dis}_response_{merged_version}_{tmp_str}.json", 'w')
json.dump(mergedResponse, f_out)
lookup_f, inferred_f = "N/A", "N/A"
lookup, inferred, full = grouping(mergedResponse)
excel_filename = f"{name_dis}_results_{date.today()}_{tmp_str}"
dis_name = disease_dict[id_dis]
df_bench = pd.read_csv('Benchmarks_12-7-2023.csv', header=0)
pos_bench = []
neg_bench = []
full_names = [[i['Drug_Name'], i['Knowledge Level'], i['Rank'], i['comp_confidence_score'],
i['comp_clinical_evidence_score'], i['comp_novelty_score'], i['sugeno_score'],
i['Sugeno_Conf_Clin'], i['Sugeno_Conf_Clin_1_5'], i['Sugeno_Conf_Clin_5_1'],
i['Sugeno_Conf_Clin_5_5'], i['ARAX'], i['ARAX_score'], i['unsecret'], i['unsecret_score'],
i['improving_agent'], i['improving_agent_score'], i['biothings_explorer'], i['biothings_explorer_score'],
i['aragorn'], i['aragorn_score']] for i in lookup]
row_rank = {}
# row_rank_list = []
for index, row in df_bench.iterrows():
if row['Disease'] == dis_name:
found = 0
for c in full_names:
if row['Drugs'].lower() == str(c[0]).lower():
tmp = c[2]
tmp_kl = c[1]
tmp_conf = c[3]
tmp_clin = c[4]
tmp_nov = c[5]
tmp_sug = c[6]
tmp_sug1 = c[7]
tmp_sug2 = c[8]
tmp_sug3 = c[9]
tmp_sug4 = c[10]
tmp_arax = c[11]
tmp_arax_score = c[12]
tmp_unsecret = c[13]
tmp_unsecret_score = c[14]
tmp_improving = c[15]
tmp_improving_score = c[16]
tmp_biothings = c[17]
tmp_biothings_score = c[18]
tmp_aragorn = c[19]
tmp_aragorn_score = c[20]
found = 1
break
if found==0:
tmp = "NOT AVAILABLE"
tmp_kl = "NOT AVAILABLE"
tmp_conf = "NOT AVAILABLE"
tmp_clin = "NOT AVAILABLE"
tmp_nov = "NOT AVAILABLE"
tmp_sug = "NOT AVAILABLE"
tmp_sug1 = "NOT AVAILABLE"
tmp_sug2 = "NOT AVAILABLE"
tmp_sug3 = "NOT AVAILABLE"
tmp_sug4 = "NOT AVAILABLE"
tmp_arax ="NOT AVAILABLE"
tmp_arax_score = "NOT AVAILABLE"
tmp_unsecret = "NOT AVAILABLE"
tmp_unsecret_score = "NOT AVAILABLE"
tmp_improving = "NOT AVAILABLE"
tmp_improving_score = "NOT AVAILABLE"
tmp_biothings = "NOT AVAILABLE"
tmp_biothings_score = "NOT AVAILABLE"
tmp_aragorn = "NOT AVAILABLE"
tmp_aragorn_score = "NOT AVAILABLE"
row_rank={
'Disease' : row['Disease'],
'Drugs': row['Drugs'],
'Benchmark': row['Benchmark'],
'Rank': tmp,
'Knowledge Level': tmp_kl,
'Confidence Score': tmp_conf,
'Clinical Evidence Score': tmp_clin,
'Novelty Score': tmp_nov,
'Sugeno Score': tmp_sug,
'Sugeno (Conf=1.0, Clinical=1.0)': tmp_sug1,
'Sugeno (Conf=1.0, Clinical=0.5)': tmp_sug2,
'Sugeno (Conf=0.5, Clinical=1.0)': tmp_sug3,
'Sugeno (Conf=0.8, Clinical=0.8)': tmp_sug4,
'ARAX': tmp_arax,
'ARAX_score': tmp_arax_score,
'unsecret': tmp_unsecret,
'unsecret_score': tmp_unsecret_score,
'improving_agent': tmp_improving,
'improving_agent_score': tmp_improving_score,
'biothings_explorer': tmp_biothings,
'biothings_explorer_score': tmp_biothings_score,
'aragorn': tmp_aragorn,
'aragorn_score': tmp_aragorn_score,
}
row_rank_list.append(row_rank)
if len(inferred) > 0:
df_inferred = pd.DataFrame(inferred)
df_inferred.to_csv(excel_filename + '_Inferred.csv', index=False)
inferred_f = excel_filename + '_Inferred.csv'
if len(lookup) > 0:
df_lookup = pd.DataFrame(lookup)
df_lookup.to_csv(excel_filename + '_Lookup.csv', index=False)
lookup_f = excel_filename + '_Lookup.csv'
atc_grouping_second(lookup_f, inferred_f, excel_filename, choice="lookup")
done=1
break
elif mergedResponse['fields']['code'] == 202:
time.sleep(100)
else:
print("Merged Version Error!")
f_out = open(f"{name_dis}_response_{merged_version}_{tmp_str}.json", 'w')
json.dump(mergedResponse, f_out)
done=1
break
elif data['status'] == 'Running':
time.sleep(100)
else:
break
if done==1:
break
df_bench_rank = pd.DataFrame(row_rank_list)
df_bench_rank.to_csv(f'Benchmarks_Rank_{date.today()}.csv', index=False)