def mvp_target_query(chemical_substance):
agent = KGAgent()
agent.mvp_target_query(chemical_substance)
graph = agent.get_graph()
chembl_id = chemical_substance
start_node = [n for n,d in graph.nodes(data=True) if 'chembl_id' in d and d['chembl_id'] == chembl_id][0]
neighbors = graph[start_node]
results = []
for neighbor in neighbors:
subgraph = graph.subgraph([start_node, neighbor])
rg = resultGraph(subgraph)
results.append(Result(result_graph=rg))
r = Message(context="translator_indigo_qa",
datetime=str(datetime.datetime.now()),
results=results)
return(r)
import pandas
from reasoner.KGAgent import KGAgent
agent = KGAgent()
# agent.cop_query('C0909381', 'C0206178')
# graph = agent.get_graph()
# print(graph.nodes(data=True))
# print(graph.edges(data=True))
cop_file = '../data/neo4j/cop_benchmark_input_cui_curated.csv'
cop = pandas.read_csv(cop_file)
for index, row in cop.iterrows():
agent.cop_query(row['drug_cui'], row['disease_cui'])
for record in agent.get_result():
print(record)
def conditionSymptomSimilarity(disease):
agent = KGAgent()
agent.conditionSymptomSimilarity(disease)
return (getDefaultResponse(agent))
def symptomToConditions(symptom):
agent = KGAgent()
agent.symptomToDisease(symptom)
return (getDefaultResponse(agent))
def conditionToSymptoms(disease):
agent = KGAgent()
agent.diseaseToSymptom(disease)
return (getDefaultResponse(agent))
def cop_query(drug, disease):
agent = KGAgent()
agent.cop_query(drug, disease)
return (getDefaultResponse(agent))
def compoundToPharmClass(chemical_substance):
agent = KGAgent()
agent.compoundToPharmClass(chemical_substance)
return (getDefaultResponse(agent))
def compoundToIndication(chemical_substance):
agent = KGAgent()
agent.compoundToIndication(chemical_substance)
return (getDefaultResponse(agent))
def geneToCompound(gene):
agent = KGAgent()
agent.geneToCompound(gene)
return (getDefaultResponse(agent))
def pathwayToGenes(pathway):
agent = KGAgent()
agent.pathwayToGenes(pathway)
return (getDefaultResponse(agent))