def answer(disease_id, use_json=False, num_show=25):
num_input_disease_symptoms = 25 # number of representative symptoms of the disease to keep
num_omim_keep = 25 # number of genetic conditions to keep
num_protein_keep = 25 # number of implicated proteins to keep
num_pathways_keep = 25 # number of pathways to keep
num_pathway_proteins_selected = 25 # number of proteins enriched for the above pathways to select
num_drugs_keep = 2 * num_show # number of drugs that target those proteins to keep
num_paths = 2 # number of paths to keep for each drug selected
# Initialize the response class
response = FormatOutput.FormatResponse(6)
response.response.table_column_names = [
"disease name", "disease ID", "drug name", "drug ID", "confidence"
]
# get the description of the disease
disease_description = RU.get_node_property(disease_id, 'name')
# Find symptoms of disease
# symptoms = RU.get_one_hop_target("disease", disease_id, "phenotypic_feature", "has_phenotype")
# symptoms_set = set(symptoms)
(symptoms_dict,
symptoms) = RU.top_n_fisher_exact([disease_id],
"disease",
"phenotypic_feature",
rel_type="has_phenotype",
n=num_input_disease_symptoms)
symptoms_set = set(symptoms)
# check for an error
if not symptoms_set:
error_message = "I found no phenotypic_features for %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# Find diseases enriched for that phenotype
path_type = [
"gene_mutations_contribute_to", "protein", "participates_in",
"pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
(genetic_diseases_dict,
genetic_diseases_selected) = RU.top_n_fisher_exact(
symptoms,
"phenotypic_feature",
"disease",
rel_type="has_phenotype",
n=num_omim_keep,
curie_prefix="OMIM",
on_path=path_type,
exclude=disease_id)
if not genetic_diseases_selected:
error_message = "I found no diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find the most representative proteins in these diseases
path_type = [
"participates_in", "pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
(implicated_proteins_dict,
implicated_proteins_selected) = RU.top_n_fisher_exact(
genetic_diseases_selected,
"disease",
"protein",
rel_type="gene_mutations_contribute_to",
n=num_protein_keep,
on_path=path_type)
if not implicated_proteins_selected:
error_message = "I found no proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find enriched pathways from those proteins
path_type = [
"participates_in", "protein", "physically_interacts_with",
"chemical_substance"
]
(pathways_selected_dict, pathways_selected) = RU.top_n_fisher_exact(
implicated_proteins_selected,
"protein",
"pathway",
rel_type="participates_in",
n=num_pathways_keep,
on_path=path_type)
if not pathways_selected:
error_message = "I found no pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find proteins enriched for those pathways
path_type = ["physically_interacts_with", "chemical_substance"]
(pathway_proteins_dict,
pathway_proteins_selected) = RU.top_n_fisher_exact(
pathways_selected,
"pathway",
"protein",
rel_type="participates_in",
n=num_pathway_proteins_selected,
on_path=path_type)
if not pathway_proteins_selected:
error_message = "I found no proteins connected to pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find drugs enriched for targeting those proteins
(drugs_selected_dict, drugs_selected) = RU.top_n_fisher_exact(
pathway_proteins_selected,
"protein",
"chemical_substance",
rel_type="physically_interacts_with",
n=num_drugs_keep)
if not drugs_selected:
error_message = "I found no drugs connected toproteins connected to pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
path_type = [
"disease", "has_phenotype", "phenotypic_feature", "has_phenotype",
"disease", "gene_mutations_contribute_to", "protein",
"participates_in", "pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
g = RU.get_subgraph_through_node_sets_known_relationships(
path_type, [[disease_id], symptoms, genetic_diseases_selected,
implicated_proteins_selected, pathways_selected,
pathway_proteins_selected, drugs_selected],
directed=True)
graph_weight_tuples = []
for drug in drugs_selected:
# get the relevant subgraph from this drug back to the input disease
node_types = [
"disease", "phenotypic_feature", "disease", "protein",
"pathway", "protein", "chemical_substance"
]
drug_pathway_protein_neighbors = RU.one_hope_neighbors_of_type(
g, drug, 'protein', 'R')
drug_pathway_neighbors = set()
for protein in drug_pathway_protein_neighbors:
drug_pathway_neighbors.update(
RU.one_hope_neighbors_of_type(g, protein, 'pathway', 'R'))
drug_protein_neighbors = set()
for pathway in drug_pathway_neighbors:
drug_protein_neighbors.update(
RU.one_hope_neighbors_of_type(g, pathway, 'protein', 'L'))
drug_disease_neighbors = set()
for protein in drug_protein_neighbors:
drug_disease_neighbors.update(
RU.one_hope_neighbors_of_type(g, protein, 'disease', 'R'))
drug_phenotype = set()
for disease in drug_disease_neighbors:
drug_phenotype.update(
RU.one_hope_neighbors_of_type(g, disease,
'phenotypic_feature', 'R'))
g2 = RU.get_subgraph_through_node_sets_known_relationships(
path_type,
[[disease_id], drug_phenotype, drug_disease_neighbors,
drug_protein_neighbors, drug_pathway_neighbors,
drug_pathway_protein_neighbors, [drug]],
directed=False)
drug_id_old_curie = drug.replace("CHEMBL.COMPOUND:CHEMBL",
"ChEMBL:")
# Machine learning probability of "treats"
prob = p.prob_single(drug_id_old_curie, disease_id)
if not prob:
prob = -1
else:
prob = prob[0]
graph_weight_tuples.append((g, prob, drug))
# sort by the path weight
graph_weight_tuples.sort(key=lambda x: x[1], reverse=True)
# print out the results
if not use_json:
num_shown = 0
for graph, weight, drug_id in graph_weight_tuples:
num_shown += 1
if num_shown > num_show:
break
drug_description = RU.get_node_property(
drug_id, "name", node_label="chemical_substance")
drug_id_old_curie = drug_id.replace("CHEMBL.COMPOUND:CHEMBL",
"ChEMBL:")
# Machine learning probability of "treats"
prob = p.prob_single(drug_id_old_curie, disease_id)
if not prob:
prob = -1
else:
prob = prob[0]
print("%s %f %f" % (drug_description, weight, prob))
else:
# add the neighborhood graph
response.add_neighborhood_graph(g.nodes(data=True),
g.edges(data=True))
response.response.table_column_names = [
"disease name", "disease ID", "drug name", "drug ID",
"path weight", "drug disease google distance",
"ML probability drug treats disease"
]
num_shown = 0
for graph, weight, drug_id in graph_weight_tuples:
num_shown += 1
if num_shown > num_show:
break
drug_description = RU.get_node_property(
drug_id, "name", node_label="chemical_substance")
drug_id_old_curie = drug_id.replace("CHEMBL.COMPOUND:CHEMBL",
"ChEMBL:")
# Machine learning probability of "treats"
prob = p.prob_single(drug_id_old_curie, disease_id)
if not prob:
prob = -1
else:
prob = prob[0]
confidence = prob
# Google distance
gd = NormGoogleDistance.get_ngd_for_all(
[drug_id, disease_id],
[drug_description, disease_description])
# populate the graph
res = response.add_subgraph(
graph.nodes(data=True),
graph.edges(data=True),
"The drug %s is predicted to treat %s." %
(drug_description, disease_description),
confidence,
return_result=True)
res.essence = "%s" % drug_description # populate with essence of question result
row_data = [] # initialize the row data
row_data.append("%s" % disease_description)
row_data.append("%s" % disease_id)
row_data.append("%s" % drug_description)
row_data.append("%s" % drug_id)
row_data.append("%f" % weight)
row_data.append("%f" % gd)
row_data.append("%f" % prob)
res.row_data = row_data
response.print()
def answer(disease_id, use_json=False, num_show=25):
num_input_disease_symptoms = 25 # number of representative symptoms of the disease to keep
num_omim_keep = 25 # number of genetic conditions to keep
num_protein_keep = 25 # number of implicated proteins to keep
num_pathways_keep = 25 # number of pathways to keep
num_pathway_proteins_selected = 25 # number of proteins enriched for the above pathways to select
num_drugs_keep = num_show # number of drugs that target those proteins to keep
num_paths = 2 # number of paths to keep for each drug selected
# Initialize the response class
response = FormatOutput.FormatResponse(6)
response.response.table_column_names = [
"disease name", "disease ID", "drug name", "drug ID", "confidence"
]
# get the description of the disease
disease_description = RU.get_node_property(disease_id, 'name')
# Find symptoms of disease
# symptoms = RU.get_one_hop_target("disease", disease_id, "phenotypic_feature", "has_phenotype")
# symptoms_set = set(symptoms)
(symptoms_dict,
symptoms) = RU.top_n_fisher_exact([disease_id],
"disease",
"phenotypic_feature",
rel_type="has_phenotype",
n=num_input_disease_symptoms)
symptoms_set = set(symptoms)
# check for an error
if not symptoms_set:
error_message = "I found no phenotypic_features for %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# Find diseases enriched for that phenotype
path_type = [
"gene_mutations_contribute_to", "protein", "participates_in",
"pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
(genetic_diseases_dict,
genetic_diseases_selected) = RU.top_n_fisher_exact(
symptoms,
"phenotypic_feature",
"disease",
rel_type="has_phenotype",
n=num_omim_keep,
curie_prefix="OMIM",
on_path=path_type,
exclude=disease_id)
if not genetic_diseases_selected:
error_message = "I found no diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find the most representative proteins in these diseases
path_type = [
"participates_in", "pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
(implicated_proteins_dict,
implicated_proteins_selected) = RU.top_n_fisher_exact(
genetic_diseases_selected,
"disease",
"protein",
rel_type="gene_mutations_contribute_to",
n=num_protein_keep,
on_path=path_type)
if not implicated_proteins_selected:
error_message = "I found no proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find enriched pathways from those proteins
path_type = [
"participates_in", "protein", "physically_interacts_with",
"chemical_substance"
]
(pathways_selected_dict, pathways_selected) = RU.top_n_fisher_exact(
implicated_proteins_selected,
"protein",
"pathway",
rel_type="participates_in",
n=num_pathways_keep,
on_path=path_type)
if not pathways_selected:
error_message = "I found no pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find proteins enriched for those pathways
path_type = ["physically_interacts_with", "chemical_substance"]
(pathway_proteins_dict,
pathway_proteins_selected) = RU.top_n_fisher_exact(
pathways_selected,
"pathway",
"protein",
rel_type="participates_in",
n=num_pathway_proteins_selected,
on_path=path_type)
if not pathway_proteins_selected:
error_message = "I found no proteins connected to pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# find drugs enriched for targeting those proteins
(drugs_selected_dict, drugs_selected) = RU.top_n_fisher_exact(
pathway_proteins_selected,
"protein",
"chemical_substance",
rel_type="physically_interacts_with",
n=num_drugs_keep)
if not drugs_selected:
error_message = "I found no drugs connected toproteins connected to pathways connected to proteins connected to diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# Next, find the most likely paths
# extract the relevant subgraph
path_type = [
"disease", "has_phenotype", "phenotypic_feature", "has_phenotype",
"disease", "gene_mutations_contribute_to", "protein",
"participates_in", "pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
g = RU.get_subgraph_through_node_sets_known_relationships(
path_type, [[disease_id], symptoms, genetic_diseases_selected,
implicated_proteins_selected, pathways_selected,
pathway_proteins_selected, drugs_selected])
# decorate graph with fisher p-values
# get dict of id to nx nodes
nx_node_to_id = nx.get_node_attributes(g, "names")
nx_id_to_node = dict()
# reverse the dictionary
for node in nx_node_to_id.keys():
id = nx_node_to_id[node]
nx_id_to_node[id] = node
i = 0
for u, v, d in g.edges(data=True):
u_id = nx_node_to_id[u]
v_id = nx_node_to_id[v]
# decorate correct nodes
# input disease to symptoms, decorated by symptom p-value
if (u_id in symptoms_set
and v_id == disease_id) or (v_id in symptoms_set
and u_id == disease_id):
try:
d["p_value"] = symptoms_dict[v_id]
except:
d["p_value"] = symptoms_dict[u_id]
continue
# symptom to disease, decorated by disease p-value
if (u_id in symptoms_set and v_id in genetic_diseases_dict) or (
v_id in symptoms_set and u_id in genetic_diseases_dict):
try:
d["p_value"] = genetic_diseases_dict[v_id]
except:
d["p_value"] = genetic_diseases_dict[u_id]
continue
# disease to protein
if (u_id in genetic_diseases_dict
and v_id in implicated_proteins_dict) or (
v_id in genetic_diseases_dict
and u_id in implicated_proteins_dict):
try:
d["p_value"] = implicated_proteins_dict[v_id]
except:
d["p_value"] = implicated_proteins_dict[u_id]
continue
# protein to pathway
if (u_id in implicated_proteins_dict
and v_id in pathways_selected_dict) or (
v_id in implicated_proteins_dict
and u_id in pathways_selected_dict):
try:
d["p_value"] = pathways_selected_dict[v_id]
except:
d["p_value"] = pathways_selected_dict[u_id]
continue
# pathway to protein
if (u_id in pathways_selected_dict
and v_id in pathway_proteins_dict) or (
v_id in pathways_selected_dict
and u_id in pathway_proteins_dict):
try:
d["p_value"] = pathway_proteins_dict[v_id]
except:
d["p_value"] = pathway_proteins_dict[u_id]
continue
# protein to drug
if (u_id in pathway_proteins_dict and v_id in drugs_selected_dict
) or (v_id in pathway_proteins_dict
and u_id in drugs_selected_dict):
try:
d["p_value"] = drugs_selected_dict[v_id]
except:
d["p_value"] = drugs_selected_dict[u_id]
continue
# otherwise, stick a p_value of 1
d["p_value"] = 1
# decorate with COHD data
RU.weight_disease_phenotype_by_cohd(
g, max_phenotype_oxo_dist=2, default_value=1
) # automatically pulls it out to top-level property
# decorate with drug->target binding probability
RU.weight_graph_with_property(
g, "probability", default_value=1,
transformation=lambda x: x) # pulls it out to top level property
# transform the graph properties so they all point the same direction
# will be finding shortest paths, so make 0=bad, 1=good transform to 0=good, 1=bad
RU.transform_graph_weight(
g,
"cohd_freq",
default_value=0,
transformation=lambda x: 1 / float(x + .001) - 1 / (1 + .001))
RU.transform_graph_weight(
g,
"probability",
default_value=0,
transformation=lambda x: 1 / float(x + .001) - 1 / (1 + .001))
# merge the graph properties (additively)
RU.merge_graph_properties(g, ["p_value", "cohd_freq", "probability"],
"merged",
operation=lambda x, y: x + y)
graph_weight_tuples = []
for drug in drugs_selected:
decorated_paths, decorated_path_edges, path_lengths = RU.get_top_shortest_paths(
g, disease_id, drug, num_paths, property='merged')
for path_ind in range(num_paths):
g2 = nx.Graph()
path = decorated_paths[path_ind]
for node_prop in path:
node_uuid = node_prop['properties']['UUID']
g2.add_node(node_uuid, **node_prop)
path = decorated_path_edges[path_ind]
for edge_prop in path:
source_uuid = edge_prop['properties']['source_node_uuid']
target_uuid = edge_prop['properties']['target_node_uuid']
g2.add_edge(source_uuid, target_uuid, **edge_prop)
graph_weight_tuples.append((g2, path_lengths[path_ind], drug))
# sort by the path weight
graph_weight_tuples.sort(key=lambda x: x[1])
# print out the results
if not use_json:
for graph, weight, drug_id in graph_weight_tuples:
drug_description = RU.get_node_property(
drug_id, "name", node_label="chemical_substance")
print("%s %f" % (drug_description, weight))
else:
response.response.table_column_names = [
"disease name", "disease ID", "drug name", "drug ID",
"path weight", "drug disease google distance",
"ML probability drug treats disease"
]
for graph, weight, drug_id in graph_weight_tuples:
drug_description = RU.get_node_property(
drug_id, "name", node_label="chemical_substance")
drug_id_old_curie = drug_id.replace("CHEMBL.COMPOUND:CHEMBL",
"ChEMBL:")
# Machine learning probability of "treats"
prob = p.prob_single(drug_id_old_curie, disease_id)
if not prob:
prob = -1
else:
prob = prob[0]
confidence = prob
# Google distance
gd = NormGoogleDistance.get_ngd_for_all(
[drug_id, disease_id],
[drug_description, disease_description])
# populate the graph
res = response.add_subgraph(
graph.nodes(data=True),
graph.edges(data=True),
"The drug %s is predicted to treat %s." %
(drug_description, disease_description),
confidence,
return_result=True)
res.essence = "%s" % drug_description # populate with essence of question result
row_data = [] # initialize the row data
row_data.append("%s" % disease_description)
row_data.append("%s" % disease_id)
row_data.append("%s" % drug_description)
row_data.append("%s" % drug_id)
row_data.append("%f" % weight)
row_data.append("%f" % gd)
row_data.append("%f" % prob)
res.row_data = row_data
response.print()