def get_similar_nodes_in_common_parameters(node_ID, target_node_label, association_node_label):
"""
This function will get the parameters for get_similar_nodes_in_common based on target node, target label, and association label
:param node_ID: source node ID (name in KG)
:param target_label: the node types that you want returned
:param association_node_label: the association node (node in common between source and target) type
:return: dict, error_code, error_message (dict keys input_node_ID, input_node_label, association_node_label, input_association_relationship,
target_association_relationship, target_node_label)
"""
# Check if node exists
if not RU.node_exists_with_property(node_ID, 'id'):
error_message = "Sorry, the disease %s is not yet in our knowledge graph." % node_ID
error_code = "DiseaseNotFound"
return dict(), error_code, error_message
# Get label/kind of node the source is
input_node_label = RU.get_node_property(node_ID, "label")
input_node_ID = node_ID
# Get relationship between source and association label
rels = RU.get_relationship_types_between(input_node_ID, input_node_label, "", association_node_label, max_path_len=1)
# TODO: there could be multiple relationship types, for now, let's just pop one
if not rels:
error_code = "NoRelationship"
error_message = "Sorry, the %s %s is not connected to any %s." % (input_node_label, input_node_ID, association_node_label)
parent = RU.get_one_hop_target(input_node_label, input_node_ID, input_node_label, "subclass_of", direction="r")
if parent:
parent = parent.pop()
error_message += "\n Note that %s is a parent of %s, so you might try that instead." % (
RU.get_node_property(parent, 'name'), RU.get_node_property(input_node_ID, 'name'))
return dict(), error_code, error_message
input_association_relationship = rels.pop()
# Get relationship between target and association label
rels = RU.get_relationship_types_between("", target_node_label, "", association_node_label, max_path_len=1)
if not rels:
error_code = "NoRelationship"
error_message = "Sorry, no %s is not connected to any %s." % (target_node_label, association_node_label)
return dict(), error_code, error_message
target_association_relationship = rels.pop()
# TODO: kludgy fix for microRNA's having multiple relationship types, only one of which shows up frequently
if target_association_relationship == "gene_mutations_contribute_to":
target_association_relationship = "gene_associated_with_condition"
# populate the arguments
arguments = dict(input_node_ID=input_node_ID,
input_node_label=input_node_label,
association_node_label=association_node_label,
input_association_relationship=input_association_relationship,
target_association_relationship=target_association_relationship,
target_node_label=target_node_label)
return arguments, None, None
def restate_question(self, input_parameters):
"""
Restates a question.
:param parameters: a dictionary with keys given by self.parameters.keys()
:return: string
"""
# First, get rid of the Nones since they substitute in an ugly way
parameters = dict()
for key, value in input_parameters.items():
if value is not None:
parameters[key] = value
# Try to get the description of each node
parameters_as_descriptions = dict()
if parameters:
for parameter in parameters:
try:
description = RU.get_node_property(parameters[parameter], 'description')
except:
description = parameters[parameter]
parameters_as_descriptions[parameter] = description
# Lastly, make the template substitution
if parameters_as_descriptions:
restated = self.restated_question_template.safe_substitute(parameters_as_descriptions)
else:
restated = self.restated_question_template.safe_substitute({})
return restated
def answer(source_node_ID,
target_node_type,
association_node_type,
use_json=False,
threshold=0.2,
n=20):
"""
Answers the question what X are similar to Y based on overlap of common Z nodes. X is target_node_type,
Y is source_node_ID, Z is association_node_type. The relationships are automatically determined in
SimilarNodesInCommon by looking for 1 hop relationships and poping the FIRST one (you are warned).
:param source_node_ID: actual name in the KG
:param target_node_type: kinds of nodes you want returned
:param association_node_type: kind of node you are computing the Jaccard overlap on
:param use_json: print the results in standardized format
:param threshold: only return results where jaccard is >= this threshold
:param n: number of results to return (default 20)
:return: reponse (or printed text)
"""
# Initialize the response class
response = FormatOutput.FormatResponse(5)
# Initialize the similar nodes class
similar_nodes_in_common = SimilarNodesInCommon.SimilarNodesInCommon()
# get the description
source_node_description = RU.get_node_property(source_node_ID,
'description')
# get the source node label
source_node_label = RU.get_node_property(source_node_ID, 'label')
# Get the nodes in common
node_jaccard_tuples_sorted, error_code, error_message = similar_nodes_in_common.get_similar_nodes_in_common_source_target_association(
source_node_ID, target_node_type, association_node_type, threshold)
# reduce to top 100
if len(node_jaccard_tuples_sorted) > n:
node_jaccard_tuples_sorted = node_jaccard_tuples_sorted[0:n]
# make sure that the input node isn't in the list
node_jaccard_tuples_sorted = [
i for i in node_jaccard_tuples_sorted if i[0] != source_node_ID
]
# check for an error
if error_code is not None or error_message is not None:
if not use_json:
print(error_message)
return
else:
response.add_error_message(error_code, error_message)
response.print()
return
# Otherwise return the results
if not use_json:
to_print = "The %s's involving similar %s's as %s are: \n" % (
target_node_type, association_node_type,
source_node_description)
for other_disease_ID, jaccard in node_jaccard_tuples_sorted:
to_print += "%s\t%s\tJaccard %f\n" % (
other_disease_ID,
RU.get_node_property(other_disease_ID,
'description'), jaccard)
print(to_print)
else:
node_jaccard_ID_sorted = [
id for id, jac in node_jaccard_tuples_sorted
]
# print(RU.return_subgraph_through_node_labels(source_node_ID, source_node_label, node_jaccard_ID_sorted, target_node_type,
# [association_node_type], with_rel=[], directed=True, debug=True))
# get the entire subgraph
g = RU.return_subgraph_through_node_labels(source_node_ID,
source_node_label,
node_jaccard_ID_sorted,
target_node_type,
[association_node_type],
with_rel=[],
directed=False,
debug=False)
# extract the source_node_number
for node, data in g.nodes(data=True):
if data['properties']['name'] == source_node_ID:
source_node_number = node
break
# Get all the target numbers
target_id2numbers = dict()
node_jaccard_ID_sorted_set = set(node_jaccard_ID_sorted)
for node, data in g.nodes(data=True):
if data['properties']['name'] in node_jaccard_ID_sorted_set:
target_id2numbers[data['properties']['name']] = node
for other_disease_ID, jaccard in node_jaccard_tuples_sorted:
to_print = "The %s %s involves similar %s's as %s with similarity value %f" % (
target_node_type,
RU.get_node_property(other_disease_ID, 'description'),
association_node_type, source_node_description, jaccard)
# get all the shortest paths between source and target
all_paths = nx.all_shortest_paths(
g, source_node_number, target_id2numbers[other_disease_ID])
# get all the nodes on these paths
try:
rel_nodes = set()
for path in all_paths:
for node in path:
rel_nodes.add(node)
if rel_nodes:
# extract the relevant subgraph
sub_g = nx.subgraph(g, rel_nodes)
# add it to the response
response.add_subgraph(sub_g.nodes(data=True),
sub_g.edges(data=True), to_print,
jaccard)
except:
pass
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 = 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 main():
parser = argparse.ArgumentParser(
description=
"Answers questions of the form: 'what pathways are most enriched by $protein_list?'",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-s',
'--source',
type=str,
help="source curie ID",
default="UniProtKB:Q96M43")
parser.add_argument('-t',
'--target',
type=str,
help="target node type",
default="pathway")
parser.add_argument('-y',
'--type',
type=str,
help="source node type",
default="protein")
parser.add_argument(
'-j',
'--json',
action='store_true',
help=
'Flag specifying that results should be printed in JSON format (to stdout)',
default=False)
parser.add_argument(
'-r',
'--rel_type',
type=str,
help='Only do the Fisher exact test along edges of this type',
default=None)
parser.add_argument(
'--describe',
action='store_true',
help='Print a description of the question to stdout and quit',
default=False)
parser.add_argument('--num_show',
type=int,
help='Maximum number of results to return',
default=20)
# Parse and check args
args = parser.parse_args()
source_arg = args.source
target_type = args.target
source_type = args.type
use_json = args.json
describe_flag = args.describe
num_show = args.num_show
rel_type = args.rel_type
if source_arg[0] == "[":
if "','" not in source_arg:
source_arg = source_arg.replace(",", "','").replace("[",
"['").replace(
"]", "']")
source_list = ast.literal_eval(source_arg)
source_list_strip = []
for source in source_list:
source_list_strip.append(source.strip())
source_list = source_list_strip
else:
source_list = [source_arg]
# Initialize the question class
Q = QuestionFisher()
if describe_flag:
res = Q.describe()
print(res)
else:
# Initialize the response class
response = FormatOutput.FormatResponse(6)
response.response.table_column_names = [
"target name", "target ID", "P value"
]
graph_weight_tuples = []
q_answer = Q.answer(source_list,
source_type,
target_type,
use_json=use_json,
num_show=num_show,
rel_type=rel_type)
if not q_answer: # if q_answer == None
return None # All messages printed out; safe to quit
p_dict, target_list = q_answer
# print out the results
if not use_json:
for target_name in target_list:
target_description = RU.get_node_property(
target_name, "name", node_label=target_type)
print("%s %f" % (target_description, p_dict[target_name]))
else:
#response.response.table_column_names = ["source name", "source ID", "target name", "target ID", "path weight",
# "target source google distance",
# "ML probability target treats source"]
for target_name in target_list:
target_description = RU.get_node_property(
target_name, "name", node_label=target_type)
target_id_old_curie = target_name.replace(
"CHEMBL.COMPOUND:CHEMBL", "ChEMBL:")
confidence = p_dict[target_name]
# populate the graph
graph = RU.get_graph_from_nodes([target_name])
res = response.add_subgraph(
graph.nodes(data=True),
graph.edges(data=True),
"The target %s is enriched by %s." %
(target_description, str(source_list)),
confidence,
return_result=True)
res.essence = "%s" % target_description # populate with essence of question result
row_data = [] # initialize the row data
#row_data.append("%s" % source_description)
#row_data.append("%s" % source_id)
row_data.append("%s" % target_description)
row_data.append("%s" % target_name)
row_data.append("%f" % confidence)
#row_data.append("%f" % gd)
#row_data.append("%f" % prob)
res.row_data = row_data
response.print()
def old_answer(disease_ID, use_json=False, threshold=0.2):
# This is about 5 times slower than the current answer, but is a bit clearer in how it's coded
# Initialize the response class
response = FormatOutput.FormatResponse(4)
# Check if node exists
if not RU.node_exists_with_property(disease_ID, 'name'):
error_message = "Sorry, the disease %s is not yet in our knowledge graph." % disease_ID
error_code = "DiseaseNotFound"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# Get label/kind of node the source is
disease_label = RU.get_node_property(disease_ID, "label")
if disease_label != "disease" and disease_label != "disease":
error_message = "Sorry, the input has label %s and needs to be one of: disease, disease." \
" Please try a different term" % disease_label
error_code = "NotADisease"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# get the description
disease_description = RU.get_node_property(disease_ID, 'description')
# get the phenotypes associated to the disease
disease_phenotypes = RU.get_one_hop_target(disease_label, disease_ID,
"phenotypic_feature",
"has_phenotype")
# Look more steps beyond if we didn't get any physically_interacts_with
if disease_phenotypes == []:
for max_path_len in range(2, 5):
disease_phenotypes = RU.get_node_names_of_type_connected_to_target(
disease_label,
disease_ID,
"phenotypic_feature",
max_path_len=max_path_len,
direction="u")
if disease_phenotypes:
break
# print("Total of %d phenotypes" % len(disease_phenotypes))
# Make sure you actually picked up at least one phenotype
if not disease_phenotypes:
error_message = "No phenotypes found for this disease."
error_code = "NoPhenotypesFound"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
disease_phenotypes_set = set(disease_phenotypes)
# get all the other disease that connect and get the phenotypes in common
other_disease_IDs_to_intersection_counts = dict()
for target_label in ["disease", "disease"]:
# direct connection
# print("direct")
node_label_list = ["phenotypic_feature"]
relationship_label_list = ["has_phenotype", "has_phenotype"]
node_of_interest_position = 0
names2counts, names2nodes = RU.count_nodes_of_type_on_path_of_type_to_label(
disease_ID, disease_label, target_label, node_label_list,
relationship_label_list, node_of_interest_position)
for ID in names2counts.keys():
if names2counts[ID] / float(
len(disease_phenotypes_set)
) >= threshold: # if it's below this threshold, no way the Jaccard index will be large enough
other_disease_IDs_to_intersection_counts[ID] = names2counts[ID]
if not other_disease_IDs_to_intersection_counts:
error_code = "NoDiseasesFound"
error_message = "No diseases were found with similarity crossing the threshold of %f." % threshold
parent = RU.get_one_hop_target(disease_label, disease_ID,
disease_label, "subclass_of").pop()
if parent:
error_message += "\n Note that %s is a parent disease to %s, so you might try that instead." % (
RU.get_node_property(parent,
'description'), disease_description)
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# print("Total number of other diseases %d" % len(list(other_disease_IDs_to_intersection_counts.keys())))
# Now for each of the diseases in here, compute the actual Jaccard index
disease_jaccard_tuples = []
# i = 0
for other_disease_ID in other_disease_IDs_to_intersection_counts.keys():
# print(i)
# i += 1
# print(other_disease_ID)
# get the phenotypes associated to the disease
if other_disease_ID.split(":")[0] == "DOID":
other_disease_label = "disease"
if other_disease_ID.split(":")[0] == "OMIM":
other_disease_label = "disease"
other_disease_phenotypes = RU.get_one_hop_target(
other_disease_label, other_disease_ID, "phenotypic_feature",
"has_phenotype")
# Look more steps beyond if we didn't get any physically_interacts_with
if other_disease_phenotypes == []:
for max_path_len in range(2, 5):
other_disease_phenotypes = RU.get_node_names_of_type_connected_to_target(
other_disease_label,
other_disease_ID,
"phenotypic_feature",
max_path_len=max_path_len,
direction="u")
if other_disease_phenotypes:
break
# compute the Jaccard index
if not other_disease_phenotypes:
jaccard = 0
else:
other_disease_phenotypes_set = set(other_disease_phenotypes)
jaccard = other_disease_IDs_to_intersection_counts[
other_disease_ID] / float(
len(
list(
disease_phenotypes_set.union(
other_disease_phenotypes_set))))
# print("jaccard %f" % jaccard)
if jaccard > threshold:
disease_jaccard_tuples.append((other_disease_ID, jaccard))
# Format the results.
# Maybe nothing passed the threshold
if not disease_jaccard_tuples:
error_code = "NoDiseasesFound"
error_message = "No diseases were found with similarity crossing the threshold of %f." % threshold
parent = RU.get_one_hop_target(disease_label, disease_ID,
disease_label, "subclass_of")
if parent:
error_message += "\n Note that %s is a parent disease to %s, so you might try that instead." % (
RU.get_node_property(parent,
'description'), disease_description)
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
return 1
# Otherwise there are results to return, first sort them largest to smallest
disease_jaccard_tuples_sorted = [(x, y) for x, y in sorted(
disease_jaccard_tuples, key=lambda pair: pair[1], reverse=True)]
if not use_json:
to_print = "The diseases similar to %s are: \n" % disease_description
for other_disease_ID, jaccard in disease_jaccard_tuples_sorted:
to_print += "%s\t%s\tJaccard %f\n" % (
other_disease_ID,
RU.get_node_property(other_disease_ID, 'description'), jaccard)
print(to_print)
else:
for other_disease_ID, jaccard in disease_jaccard_tuples_sorted:
to_print = "%s is similar to the disease %s with similarity value %f" % (
disease_description,
RU.get_node_property(other_disease_ID, 'decription'), jaccard)
g = RU.get_node_as_graph(other_disease_ID)
response.add_subgraph(g.nodes(data=True), g.edges(data=True),
to_print, jaccard)
response.print()
def answer(self, disease_ID, use_json=False, threshold=0.2):
"""
Answer the question: what other diseases have similarity >= jaccard=0.2 with the given disease_ID
(in terms of phenotype overlap)
:param disease_ID: KG disease name (eg. DOID:8398)
:param use_json: use the standardized output format
:param threshold: only include diseases with Jaccard index above this
:return: None (print to stdout), unless there's an error, then return 1
"""
# Initialize the response class
response = FormatOutput.FormatResponse(4)
# Check if node exists
if not RU.node_exists_with_property(disease_ID, 'name'):
error_message = "Sorry, the disease %s is not yet in our knowledge graph." % disease_ID
error_code = "DiseaseNotFound"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# Get label/kind of node the source is
disease_label = RU.get_node_property(disease_ID, "label")
if disease_label != "disease" and disease_label != "disease":
error_message = "Sorry, the input has label %s and needs to be one of: disease, disease." \
" Please try a different term" % disease_label
error_code = "NotADisease"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# get the description
disease_description = RU.get_node_property(disease_ID, 'description')
# get the phenotypes associated to the disease
disease_phenotypes = RU.get_one_hop_target(disease_label, disease_ID,
"phenotypic_feature",
"has_phenotype")
# Look more steps beyond if we didn't get any physically_interacts_with
if disease_phenotypes == []:
for max_path_len in range(2, 5):
disease_phenotypes = RU.get_node_names_of_type_connected_to_target(
disease_label,
disease_ID,
"phenotypic_feature",
max_path_len=max_path_len,
direction="u")
if disease_phenotypes:
break
# Make sure you actually picked up at least one phenotype
if not disease_phenotypes:
error_message = "No phenotypes found for this disease."
error_code = "NoPhenotypesFound"
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
disease_phenotypes_set = set(disease_phenotypes)
# get all the other disease that connect and get the phenotypes in common
# direct connection
node_label_list = ["phenotypic_feature"]
relationship_label_list = ["has_phenotype", "has_phenotype"]
node_of_interest_position = 0
other_disease_IDs_to_intersection_counts = dict()
for target_label in ["disease", "disease"]:
names2counts, names2nodes = RU.count_nodes_of_type_on_path_of_type_to_label(
disease_ID, disease_label, target_label, node_label_list,
relationship_label_list, node_of_interest_position)
for ID in names2counts.keys():
if names2counts[ID] / float(
len(disease_phenotypes_set)
) >= threshold: # if it's below this threshold, no way the Jaccard index will be large enough
other_disease_IDs_to_intersection_counts[
ID] = names2counts[ID]
# check if any other diseases passed the threshold
if not other_disease_IDs_to_intersection_counts:
error_code = "NoDiseasesFound"
error_message = "No diseases were found with similarity crossing the threshold of %f." % threshold
parent = RU.get_one_hop_target(disease_label,
disease_ID,
disease_label,
"subclass_of",
direction="r")
if parent:
parent = parent.pop()
error_message += "\n Note that %s is a parent disease to %s, so you might try that instead." % (
RU.get_node_property(parent,
'description'), disease_description)
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
response.print()
return 1
# Now for each of the diseases connecting to source, count number of phenotypes
node_label_list = ["phenotypic_feature"]
relationship_label_list = ["has_phenotype", "has_phenotype"]
node_of_interest_position = 0
other_doid_counts = RU.count_nodes_of_type_for_nodes_that_connect_to_label(
disease_ID, disease_label, "disease", node_label_list,
relationship_label_list, node_of_interest_position)
other_omim_counts = RU.count_nodes_of_type_for_nodes_that_connect_to_label(
disease_ID, disease_label, "disease", node_label_list,
relationship_label_list, node_of_interest_position)
# union the two
other_disease_counts = dict()
for key in other_doid_counts.keys():
other_disease_counts[key] = other_doid_counts[key]
for key in other_omim_counts.keys():
other_disease_counts[key] = other_omim_counts[key]
# then compute the jaccard index
disease_jaccard_tuples = []
for other_disease_ID in other_disease_counts.keys():
jaccard = 0
if other_disease_ID in other_disease_IDs_to_intersection_counts:
union_card = len(disease_phenotypes) + other_disease_counts[other_disease_ID] - \
other_disease_IDs_to_intersection_counts[other_disease_ID]
jaccard = other_disease_IDs_to_intersection_counts[
other_disease_ID] / float(union_card)
if jaccard > threshold:
disease_jaccard_tuples.append((other_disease_ID, jaccard))
# Format the results.
# Maybe nothing passed the threshold
if not disease_jaccard_tuples:
error_code = "NoDiseasesFound"
error_message = "No diseases were found with similarity crossing the threshold of %f." % threshold
parent = RU.get_one_hop_target(disease_label,
disease_ID,
disease_label,
"subclass_of",
direction="r")
if parent:
parent = parent.pop()
error_message += "\n Note that %s is a parent disease to %s, so you might try that instead." % (
RU.get_node_property(parent,
'description'), disease_description)
if not use_json:
print(error_message)
return 1
else:
response.add_error_message(error_code, error_message)
return 1
# Otherwise there are results to return, first sort them largest to smallest
disease_jaccard_tuples_sorted = [(x, y) for x, y in sorted(
disease_jaccard_tuples, key=lambda pair: pair[1], reverse=True)]
if not use_json:
to_print = "The diseases with phenotypes similar to %s are: \n" % disease_description
for other_disease_ID, jaccard in disease_jaccard_tuples_sorted:
to_print += "%s\t%s\tJaccard %f\n" % (
other_disease_ID,
RU.get_node_property(other_disease_ID,
'description'), jaccard)
print(to_print)
else:
for other_disease_ID, jaccard in disease_jaccard_tuples_sorted:
to_print = "%s is phenotypically similar to the disease %s with similarity value %f" % (
disease_description,
RU.get_node_property(other_disease_ID,
'description'), jaccard)
g = RU.get_node_as_graph(other_disease_ID)
response.add_subgraph(g.nodes(data=True), g.edges(data=True),
to_print, jaccard)
response.print()
def answer(disease_id, use_json=False, num_show=20):
num_diseases_to_select = 10 # number of diseases with shared phenotypes to keep
num_omim_keep = 10 # number of genetic conditions to keep
num_proteins_keep = 10 # number of proteins implicated in diseases to keep
num_pathways_keep = 10 # number of relevant pathways to keep
num_proteins_in_pathways_keep = 10 # number of proteins in those pathways to keep
num_drugs_keep = 10 # number of drugs that target those proteins to keep
# The kinds of paths we're looking for
path_type = [
"gene_mutations_contribute_to", "protein", "participates_in",
"pathway", "participates_in", "protein",
"physically_interacts_with", "chemical_substance"
]
# Initialize the response class
response = FormatOutput.FormatResponse(6)
# get the description of the disease
disease_description = RU.get_node_property(disease_id, 'name')
# What are the defining symptoms of the disease?
# get diseases that have many raw symptoms in common
# select top N of them
# get subraph of these with the input disease
# weight by COHD data
# pick diseases with maximal (since frequency) average distance i.e. maximal expected graph distance
# get disease that have many raw symptoms in common
similar_nodes_in_common = SimilarNodesInCommon.SimilarNodesInCommon()
node_jaccard_tuples_sorted, error_code, error_message = similar_nodes_in_common.get_similar_nodes_in_common_source_target_association(
disease_id, "disease", "phenotypic_feature", 0)
# select the omims
diseases_selected = []
for n, j in node_jaccard_tuples_sorted:
if n.split(":")[0] == "OMIM":
diseases_selected.append(n)
# if we found no genetic conditions, add error message and quit
if not diseases_selected:
response.add_error_message(
"NoGeneticConditions",
"There appears to be no genetic conditions with phenotypes in common with %s"
% disease_description)
response.print()
return
# subset to top N omims that actually have the relationship types that we want:
num_selected = 0
diseases_selected_on_desired_path = []
for selected_disease in diseases_selected:
if RU.paths_of_type_source_fixed_target_free_exists(
selected_disease, "disease", path_type, limit=1):
diseases_selected_on_desired_path.append(selected_disease)
num_selected += 1
if num_selected >= num_omim_keep:
break
diseases_selected = diseases_selected_on_desired_path
# Find most representative symptoms by consulting COHD. TODO: see if this actually helps anything
# get subgraph of these with the input disease
# get all symptoms of input disease
# all_symptoms = set()
# for selected_disease in diseases_selected:
# intermediate_phenotypes = RU.get_intermediate_node_ids(disease_id, "disease", "has_phenotype", "phenotypic_feature", "has_phenotype", selected_disease, "disease")
# all_symptoms.update(intermediate_phenotypes)
# turn it back into a list
# all_symptoms = list(all_symptoms)
# get the subgraph of all relevant symptoms, the omims selected, and the input disease
# g = RU.get_graph_from_nodes(all_symptoms + diseases_selected + [disease_id], edges=True)
# weight by COHD data (if you want to)
# RU.weight_disease_phenotype_by_cohd(g, max_phenotype_oxo_dist=2)
# sort by COHD freq
# disease_path_weight_sorted = RU.get_sorted_path_weights_disease_to_disease(g, disease_id)
# genetic_diseases_selected = []
# num_omim = 0
# for id, weight in disease_path_weight_sorted:
# if id.split(":")[0] == "OMIM":
# genetic_diseases_selected.append(id)
# num_omim += 1
# if num_omim >= num_omim_keep:
# break
# in the mean-time, use them all
genetic_diseases_selected = diseases_selected
# select representative diseases
# Do nothing for now (use all of them)
# get drugs that are connected along the paths we want and count how many such paths there are
genetic_diseases_to_chemical_substance_dict = dict()
for selected_disease in genetic_diseases_selected:
res = RU.count_paths_of_type_source_fixed_target_free(
selected_disease, "disease", path_type, limit=num_drugs_keep)
# add it to our dictionary
genetic_diseases_to_chemical_substance_dict[selected_disease] = res
# get the unique drugs
drug_counts_tuples = [
item
for items in genetic_diseases_to_chemical_substance_dict.values()
for item in items
]
drugs_path_counts = dict()
for drug, count in drug_counts_tuples:
if drug not in drugs_path_counts:
drugs_path_counts[drug] = count
else:
drugs_path_counts[drug] += count
# put them as tuples in a list, sorted by the ones with the most paths
drugs_path_counts_tuples = []
for drug in drugs_path_counts.keys():
count = drugs_path_counts[drug]
drugs_path_counts_tuples.append((drug, count))
drugs_path_counts_tuples.sort(key=lambda x: x[1], reverse=True)
if not use_json:
#for drug, count in drugs_path_counts_tuples:
# name = RU.get_node_property(drug, "name", node_label="chemical_substance")
# print("%s (%s): %d" % (name, drug, count))
print("source,target")
for drug, count in drugs_path_counts_tuples:
drug_old_curie = drug.split(":")[1].replace("L", "L:").replace(
"H", "h")
print("%s,%s" % (drug_old_curie, disease_id))
def answer(source_node_ID,
target_node_type,
association_node_type,
use_json=False,
threshold=0.2,
n=20):
"""
Answers the question what X are similar to Y based on overlap of common Z nodes. X is target_node_type,
Y is source_node_ID, Z is association_node_type. The relationships are automatically determined in
SimilarNodesInCommon by looking for 1 hop relationships and poping the FIRST one (you are warned).
:param source_node_ID: actual name in the KG
:param target_node_type: kinds of nodes you want returned
:param association_node_type: kind of node you are computing the Jaccard overlap on
:param use_json: print the results in standardized format
:param threshold: only return results where jaccard is >= this threshold
:param n: number of results to return (default 20)
:return: reponse (or printed text)
"""
# Initialize the response class
response = FormatOutput.FormatResponse(5)
# add the column names for the row data
response.message.table_column_names = [
"source name", "source ID", "target name", "target ID",
"Jaccard index"
]
# Initialize the similar nodes class
similar_nodes_in_common = SimilarNodesInCommon.SimilarNodesInCommon()
# get the description
source_node_description = RU.get_node_property(source_node_ID, 'name')
# get the source node label
source_node_label = RU.get_node_property(source_node_ID, 'label')
# Get the nodes in common
node_jaccard_tuples_sorted, error_code, error_message = similar_nodes_in_common.get_similar_nodes_in_common_source_target_association(
source_node_ID, target_node_type, association_node_type, threshold)
# reduce to top 100
if len(node_jaccard_tuples_sorted) > n:
node_jaccard_tuples_sorted = node_jaccard_tuples_sorted[0:n]
# make sure that the input node isn't in the list
node_jaccard_tuples_sorted = [
i for i in node_jaccard_tuples_sorted if i[0] != source_node_ID
]
# check for an error
if error_code is not None or error_message is not None:
if not use_json:
print(error_message)
return
else:
response.add_error_message(error_code, error_message)
response.print()
return
#### If use_json not specified, then return results as a fairly plain list
if not use_json:
to_print = "The %s's involving similar %ss as %s are: \n" % (
target_node_type, association_node_type,
source_node_description)
for other_disease_ID, jaccard in node_jaccard_tuples_sorted:
to_print += "%s\t%s\tJaccard %f\n" % (
other_disease_ID,
RU.get_node_property(other_disease_ID, 'name'), jaccard)
print(to_print)
#### Else if use_json requested, return the results in the Translator standard API JSON format
else:
#### Create the QueryGraph for this type of question
query_graph = QueryGraph()
source_node = QNode()
source_node.id = "n00"
source_node.curie = source_node_ID
source_node.type = source_node_label
association_node = QNode()
association_node.id = "n01"
association_node.type = association_node_type
association_node.is_set = True
target_node = QNode()
target_node.id = "n02"
target_node.type = target_node_type
query_graph.nodes = [source_node, association_node, target_node]
#source_association_relationship_type = "unknown1"
edge1 = QEdge()
edge1.id = "en00-n01"
edge1.source_id = "n00"
edge1.target_id = "n01"
#edge1.type = source_association_relationship_type
#association_target_relationship_type = "unknown2"
edge2 = QEdge()
edge2.id = "en01-n02"
edge2.source_id = "n01"
edge2.target_id = "n02"
#edge2.type = association_target_relationship_type
query_graph.edges = [edge1, edge2]
#### DONT Suppress the query_graph because we can now do the knowledge_map with v0.9.1
response.message.query_graph = query_graph
#### Create a mapping dict with the source curie and node types and edge types. This dict is used for reverse lookups by type
#### for mapping to the QueryGraph. There is a potential point of failure here if there are duplicate node or edge types. FIXME
response._type_map = dict()
response._type_map[source_node.curie] = source_node.id
response._type_map[association_node.type] = association_node.id
response._type_map[target_node.type] = target_node.id
response._type_map["e" + edge1.source_id + "-" +
edge1.target_id] = edge1.id
response._type_map["e" + edge2.source_id + "-" +
edge2.target_id] = edge2.id
#### Extract the sorted IDs from the list of tuples
node_jaccard_ID_sorted = [
id for id, jac in node_jaccard_tuples_sorted
]
# print(RU.return_subgraph_through_node_labels(source_node_ID, source_node_label, node_jaccard_ID_sorted, target_node_type,
# [association_node_type], with_rel=[], directed=True, debug=True))
# get the entire subgraph
g = RU.return_subgraph_through_node_labels(source_node_ID,
source_node_label,
node_jaccard_ID_sorted,
target_node_type,
[association_node_type],
with_rel=[],
directed=False,
debug=False)
# extract the source_node_number
for node, data in g.nodes(data=True):
if data['properties']['id'] == source_node_ID:
source_node_number = node
break
# Get all the target numbers
target_id2numbers = dict()
node_jaccard_ID_sorted_set = set(node_jaccard_ID_sorted)
for node, data in g.nodes(data=True):
if data['properties']['id'] in node_jaccard_ID_sorted_set:
target_id2numbers[data['properties']['id']] = node
for other_disease_ID, jaccard in node_jaccard_tuples_sorted:
target_name = RU.get_node_property(other_disease_ID, 'name')
to_print = "The %s %s involves similar %ss as %s with similarity value %f" % (
target_node_type, target_name, association_node_type,
source_node_description, jaccard)
# get all the shortest paths between source and target
all_paths = nx.all_shortest_paths(
g, source_node_number, target_id2numbers[other_disease_ID])
# get all the nodes on these paths
#try:
if 1 == 1:
rel_nodes = set()
for path in all_paths:
for node in path:
rel_nodes.add(node)
if rel_nodes:
# extract the relevant subgraph
sub_g = nx.subgraph(g, rel_nodes)
# add it to the response
res = response.add_subgraph(sub_g.nodes(data=True),
sub_g.edges(data=True),
to_print,
jaccard,
return_result=True)
res.essence = "%s" % target_name # populate with essence of question result
res.essence_type = target_node_type
row_data = [] # initialize the row data
row_data.append("%s" % source_node_description)
row_data.append("%s" % source_node_ID)
row_data.append("%s" % target_name)
row_data.append("%s" % other_disease_ID)
row_data.append("%f" % jaccard)
res.row_data = row_data
# except:
# pass
response.print()
def get_parameters(self, input_question):
"""
Given the input_question, try to extract the proper parameters
:param input_question: plain text input question
:return: a dictionary (with keys self.parameter_names), values either None or the KG node names
"""
parameters = dict()
for parameter in self.parameter_names:
parameters[parameter] = None
# The "what is a X?" questions are of a completely different form and are handled separately
if self.parameter_names == ["term"]:
# Next, see if it's a "what is" question
term = None
input_question = re.sub("\?", "", input_question)
input_question = re.sub("^\s+", "", input_question)
input_question = re.sub("\s+$", "", input_question)
input_question = input_question.lower()
match = re.match("what is\s*(a|an)?\s+(.+)", input_question, re.I)
if match:
term = match.group(2)
term = re.sub("^\s+", "", term)
term = re.sub("\s+$", "", term)
parameters["term"] = term
return parameters
match = re.match("what are (.+)", input_question, re.I)
if match:
term = match.group(1)
term = re.sub("^\s+", "", term)
term = re.sub("\s+$", "", term)
parameters["term"] = term
return parameters
else:
return parameters
else: # Otherwise, it's a standard question template
# get all n-tuples of words in the question (largest to smallest)
blocks = []
question_tokenized = nltk.word_tokenize(input_question, "english")
# Tokenizers have a bad habit of splitting on \', so fix it
question_tokenized_no_apos_split = []
for ind, block in enumerate(question_tokenized):
if block[0] == "'" and ind > 0: # the tokenizer split on apostrophe
question_tokenized_no_apos_split[ind - 1] += question_tokenized[ind]
else:
question_tokenized_no_apos_split.append(block)
question_tokenized = question_tokenized_no_apos_split
for block_size in range(1, len(question_tokenized)):
for i in range(len(question_tokenized) - block_size + 1):
block = " ".join(question_tokenized[i:(i + block_size)])
blocks.append(block)
blocks = list(reversed(blocks))
# Look for anything that could be a node name
candidate_node_names = []
found_blocks = [] # keep track of the already found blocks TODO: this will cause problems when you ask something like "how are malaria and mixed malaria different?"
for block in blocks:
nodes = find_node_name(block)
if nodes:
if all([block not in b for b in found_blocks]): # only add it if it's not a proper subset of an already found block
candidate_node_names.extend(nodes)
found_blocks.append(block)
#print(block)
# Get the node labels for the found nodes
candidate_node_names_labels = set() # set automatically deduplicates for me
for node in candidate_node_names:
node_label = RU.get_node_property(node, "label") # TODO: Arnab's UMLS lookup
candidate_node_names_labels.add((node, node_label))
# turn it back into a set for indexing
candidate_node_names_labels = list(candidate_node_names_labels)
# For each of the parameter names, make sure it only shows up once, and if so, populate it
for parameter_name in self.parameter_names:
parameter_name_positions = []
pos = 0
for node, node_label in candidate_node_names_labels:
if node_label == parameter_name:
parameter_name_positions.append(pos)
pos += 1
if len(parameter_name_positions) > 1:
raise CustomExceptions.MultipleTerms(parameter_name, [candidate_node_names_labels[pos][0] for pos in parameter_name_positions])
elif len(parameter_name_positions) == 0:
pass
else: # There's exactly one term
pos = parameter_name_positions.pop()
parameters[parameter_name] = candidate_node_names_labels[pos][0]
# Throw in the extra parameters
for key, value in self.other_parameters.items():
parameters[key] = value
return parameters
def answer(self,
source_name,
target_label,
relationship_type,
use_json=False):
"""
Answer a question of the type "What proteins does drug X target" but is general:
what <node X type> does <node Y grounded> <relatioship Z> that can be answered in one hop in the KG (increasing the step size if necessary).
:param query_terms: a triple consisting of a source node name (KG neo4j node name, the target label (KG neo4j
"node label") and the relationship type (KG neo4j "Relationship type")
:param source_name: KG neo4j node name (eg "carbetocin")
:param target_label: KG node label (eg. "protein")
:param relationship_type: KG relationship type (eg. "directly_interacts_with")
:param use_json: If the answer should be in Eric's Json standardized API output format
:return: list of dictionaries containing the nodes that are one hop (along relationship type) that connect source to target.
"""
# Get label/kind of node the source is
source_label = RU.get_node_property(source_name, "label")
# Get the subgraph (all targets along relationship)
has_intermediate_node = False
try:
g = RU.return_subgraph_paths_of_type(source_name,
source_label,
None,
target_label,
[relationship_type],
directed=False)
except CustomExceptions.EmptyCypherError:
try:
has_intermediate_node = True
g = RU.return_subgraph_paths_of_type(
source_name,
source_label,
None,
target_label, ['subclass_of', relationship_type],
directed=False)
except CustomExceptions.EmptyCypherError:
error_message = "No path between %s and %s via relationship %s" % (
source_name, target_label, relationship_type)
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
return response
# extract the source_node_number
for node, data in g.nodes(data=True):
if data['properties']['name'] == source_name:
source_node_number = node
break
# Get all the target numbers
target_numbers = []
for node, data in g.nodes(data=True):
if data['properties']['name'] != source_name:
target_numbers.append(node)
# if there's an intermediate node, get the name
if has_intermediate_node:
neighbors = list(g.neighbors(source_node_number))
if len(neighbors) > 1:
error_message = "More than one intermediate node"
error_code = "AmbiguousPath"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
return response
else:
intermediate_node = neighbors.pop()
# Format the results.
if not use_json:
results_list = list()
for target_number in target_numbers:
data = g.node[target_number]
results_list.append({
'type':
list(set(data['labels']) - {'Base'}).pop(),
'name':
data['properties']['name'],
'desc':
data['properties']['description'],
'prob':
1
}) # All these are known to be true
return results_list
else: # You want the standardized API output format
response = FormatOutput.FormatResponse(3) # it's a Q3 question
source_description = g.node[source_node_number]['properties'][
'description']
for target_number in target_numbers:
target_description = g.node[target_number]['properties'][
'description']
if not has_intermediate_node:
subgraph = g.subgraph([source_node_number, target_number])
else:
subgraph = g.subgraph(
[source_node_number, intermediate_node, target_number])
response.add_subgraph(
subgraph.nodes(data=True), subgraph.edges(data=True),
"%s and %s are connected by the relationship %s" %
(source_description, target_description,
relationship_type), 1)
return response
def answer(self,
source_name,
target_label,
relationship_type,
use_json=False,
directed=False):
"""
Answer a question of the type "What proteins does drug X target" but is general:
what <node X type> does <node Y grounded> <relatioship Z> that can be answered in one hop in the KG (increasing the step size if necessary).
:param query_terms: a triple consisting of a source node name (KG neo4j node name, the target label (KG neo4j
"node label") and the relationship type (KG neo4j "Relationship type")
:param source_name: KG neo4j node name (eg "carbetocin")
:param target_label: KG node label (eg. "protein")
:param relationship_type: KG relationship type (eg. "physically_interacts_with")
:param use_json: If the answer should be in Eric's Json standardized API output format
:return: list of dictionaries containing the nodes that are one hop (along relationship type) that connect source to target.
"""
# Get label/kind of node the source is
source_label = RU.get_node_property(source_name, "label")
# Get the subgraph (all targets along relationship)
has_intermediate_node = False
try:
g = RU.return_subgraph_paths_of_type(source_name,
source_label,
None,
target_label,
[relationship_type],
directed=directed)
except CustomExceptions.EmptyCypherError:
try:
has_intermediate_node = True
g = RU.return_subgraph_paths_of_type(
source_name,
source_label,
None,
target_label, ['subclass_of', relationship_type],
directed=directed)
except CustomExceptions.EmptyCypherError:
error_message = "No path between %s and %s via relationship %s" % (
source_name, target_label, relationship_type)
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
return response
# extract the source_node_number
for node, data in g.nodes(data=True):
if data['properties']['id'] == source_name:
source_node_number = node
break
# Get all the target numbers
target_numbers = []
for node, data in g.nodes(data=True):
if data['properties']['id'] != source_name:
target_numbers.append(node)
# if there's an intermediate node, get the name
if has_intermediate_node:
neighbors = list(g.neighbors(source_node_number))
if len(neighbors) > 1:
error_message = "More than one intermediate node"
error_code = "AmbiguousPath"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
return response
else:
intermediate_node = neighbors.pop()
#### If use_json not specified, then return results as a fairly plain list
if not use_json:
results_list = list()
for target_number in target_numbers:
data = g.nodes[target_number]
results_list.append({
'type':
list(set(data['labels']) - {'Base'}).pop(),
'name':
data['properties']['name'],
'desc':
data['properties']['name'],
'prob':
1
}) # All these are known to be true
return results_list
#### Else if use_json requested, return the results in the Translator standard API JSON format
else:
response = FormatOutput.FormatResponse(3) # it's a Q3 question
response.message.table_column_names = [
"source name", "source ID", "target name", "target ID"
]
source_description = g.nodes[source_node_number]['properties'][
'name']
#### Create the QueryGraph for this type of question
query_graph = QueryGraph()
source_node = QNode()
source_node.id = "n00"
source_node.curie = g.nodes[source_node_number]['properties']['id']
source_node.type = g.nodes[source_node_number]['properties'][
'category']
target_node = QNode()
target_node.id = "n01"
target_node.type = target_label
query_graph.nodes = [source_node, target_node]
edge1 = QEdge()
edge1.id = "e00"
edge1.source_id = "n00"
edge1.target_id = "n01"
edge1.type = relationship_type
query_graph.edges = [edge1]
response.message.query_graph = query_graph
#### Create a mapping dict with the source curie and the target type. This dict is used for reverse lookups by type
#### for mapping to the QueryGraph.
response._type_map = dict()
response._type_map[source_node.curie] = source_node.id
response._type_map[target_node.type] = target_node.id
response._type_map[edge1.type] = edge1.id
#### Loop over all the returned targets and put them into the response structure
for target_number in target_numbers:
target_description = g.nodes[target_number]['properties'][
'name']
if not has_intermediate_node:
subgraph = g.subgraph([source_node_number, target_number])
else:
subgraph = g.subgraph(
[source_node_number, intermediate_node, target_number])
res = response.add_subgraph(
subgraph.nodes(data=True),
subgraph.edges(data=True),
"%s and %s are connected by the relationship %s" %
(source_description, target_description,
relationship_type),
1,
return_result=True)
res.essence = "%s" % target_description # populate with essence of question result
res.essence_type = g.nodes[target_number]['properties'][
'category'] # populate with the type of the essence of question result
row_data = [] # initialize the row data
row_data.append("%s" % source_description)
row_data.append(
"%s" % g.nodes[source_node_number]['properties']['id'])
row_data.append("%s" % target_description)
row_data.append("%s" %
g.nodes[target_number]['properties']['id'])
res.row_data = row_data
return response
def get_similar_nodes_in_common(input_node_ID, input_node_label, association_node_label, input_association_relationship, target_association_relationship, target_node_label, threshold=0.2): """ This function returns the nodes that are associated with an input node based on Jaccard index similarity of shared intermediate nodes :param input_node_ID: input node ID (in KG) :param input_node_label: label of the input node :param association_node_label: what kind of node you want to calculate the Jaccard index with :param input_association_relationship: how the input node is connected to the association nodes :param target_association_relationship: how the target node is connected to the association node :param target_node_label: what kind of target nodes to return :param threshold: threshold to compute the Jaccard index :return: a list of tuples, an error_code, and an error_message. tuple[0] is a target node with tuple[1] jaccard index based on association nodes """ # get the description input_node_description = RU.get_node_property(input_node_ID, 'name') # get the nodes associated to the input node input_node_associated_nodes = RU.get_one_hop_target(input_node_label, input_node_ID, association_node_label, input_association_relationship) # Look more steps beyond if we didn't get any physically_interacts_with if input_node_associated_nodes == []: for max_path_len in range(2, 5): input_node_associated_nodes = RU.get_node_names_of_type_connected_to_target(input_node_label, input_node_ID, association_node_label, max_path_len=max_path_len, direction="u") if input_node_associated_nodes: break # Make sure you actually picked up at least one associated node if not input_node_associated_nodes: error_code = "NoNodesFound" error_message = "No %s found for %s." % (association_node_label, input_node_description) return [], error_code, error_message input_node_associated_nodes_set = set(input_node_associated_nodes) # get all the other disease that connect and get the association nodes in common # direct connection node_label_list = [association_node_label] relationship_label_list = [input_association_relationship, target_association_relationship] node_of_interest_position = 0 other_node_IDs_to_intersection_counts = dict() #if target_node_label == "disease" or target_node_label == "disease": # target_labels = ["disease", "disease"] #else: target_labels = [target_node_label] for target_label in target_labels: names2counts, names2nodes = RU.count_nodes_of_type_on_path_of_type_to_label(input_node_ID, input_node_label, target_label, node_label_list, relationship_label_list, node_of_interest_position) for ID in names2counts.keys(): if names2counts[ID] / float(len( input_node_associated_nodes_set)) >= threshold: # if it's below this threshold, no way the Jaccard index will be large enough other_node_IDs_to_intersection_counts[ID] = names2counts[ID] # check if any other associated nodes passed the threshold if not other_node_IDs_to_intersection_counts: error_code = "NoNodesFound" error_message = "No %s were found with similarity crossing the threshold of %f." % (target_node_label, threshold) parent = RU.get_one_hop_target(input_node_label, input_node_ID, input_node_label, "subclass_of", direction="r") if parent: parent = parent.pop() error_message += "\n Note that %s is a parent of %s, so you might try that instead." % ( RU.get_node_property(parent, 'name'), input_node_description) return [], error_code, error_message # Now for each of the nodes connecting to source, count number of association nodes node_label_list = [association_node_label] relationship_label_list = [input_association_relationship, target_association_relationship] node_of_interest_position = 0 other_node_counts = dict() for target_label in target_labels: temp_other_counts = RU.count_nodes_of_type_for_nodes_that_connect_to_label(input_node_ID, input_node_label, target_label, node_label_list, relationship_label_list, node_of_interest_position) # add it to the dictionary for key in temp_other_counts.keys(): other_node_counts[key] = temp_other_counts[key] # then compute the jaccard index node_jaccard_tuples = [] for other_node_ID in other_node_counts.keys(): jaccard = 0 if other_node_ID in other_node_IDs_to_intersection_counts: union_card = len(input_node_associated_nodes) + other_node_counts[other_node_ID] - \ other_node_IDs_to_intersection_counts[other_node_ID] jaccard = other_node_IDs_to_intersection_counts[other_node_ID] / float(union_card) if jaccard > threshold: node_jaccard_tuples.append((other_node_ID, jaccard)) # Format the results. # Maybe nothing passed the threshold if not node_jaccard_tuples: error_code = "NoNodesFound" error_message = "No %s's were found with similarity crossing the threshold of %f." % (target_node_label, threshold) parent = RU.get_one_hop_target(input_node_label, input_node_ID, input_node_label, "subclass_of", direction="r") if parent: parent = parent.pop() error_message += "\n Note that %s is a parent of %s, so you might try that instead." % (RU.get_node_property(parent, 'description'), input_node_description) return [], error_code, error_message # Otherwise there are results to return, first sort them largest to smallest node_jaccard_tuples_sorted = [(x, y) for x, y in sorted(node_jaccard_tuples, key=lambda pair: pair[1], reverse=True)] return node_jaccard_tuples_sorted, None, None
def answer(disease_id,
use_json=False,
num_show=20,
rev=True,
normalize=False):
"""
"""
# Initialize the response class
response = FormatOutput.FormatResponse(6)
# get the description
disease_description = RU.get_node_property(disease_id, 'name')
# get subgraph of all all the symptom nodes connecting to the disease
try:
g = RU.return_subgraph_paths_of_type(disease_id,
"disease",
None,
"phenotypic_feature",
["has_phenotype"],
directed=False)
except CustomExceptions.EmptyCypherError:
error_code = "EmptyGraph"
error_message = "Sorry, but there are no phenotypes associated to %s" % disease_description
response.add_error_message(error_code, error_message)
response.print()
return 1
# decorate with cohd data
RU.weight_graph_with_cohd_frequency(
g, normalized=normalize
) # TODO: check if normalized on returns better results
# sort the phenotypes by frequency
names = nx.get_node_attributes(g, 'names')
labels = nx.get_node_attributes(g, 'labels')
descriptions = nx.get_node_attributes(g, 'description')
# get the node corresponding to the disease
disease_node = None
for node in names.keys():
if names[node] == disease_id:
disease_node = node
# get all the nodes and the frequencies in one place
node_freq_tuples = []
for node in names.keys():
if "phenotypic_feature" == list(set(labels[node]) -
{"Base"}).pop():
# get the corresponding edge frequency (try both directions)
edge_data = g.get_edge_data(disease_node, node)
if "cohd_freq" in edge_data and isinstance(
edge_data["cohd_freq"], float):
freq = edge_data["cohd_freq"]
else:
edge_data = g.get_edge_data(node, disease_node)
if "cohd_freq" in edge_data and isinstance(
edge_data["cohd_freq"], float):
freq = edge_data["cohd_freq"]
else:
freq = 0
node_freq_tuples.append((node, freq))
# sort the node freqs
node_freq_tuples_sorted = sorted(node_freq_tuples,
key=lambda x: x[1],
reverse=rev)
# reduce to top n
node_freq_tuples_sorted_top_n = node_freq_tuples_sorted
if len(node_freq_tuples_sorted_top_n) > num_show:
node_freq_tuples_sorted_top_n = node_freq_tuples_sorted_top_n[
0:num_show]
# good nodes
good_nodes = set([tup[0] for tup in node_freq_tuples_sorted_top_n])
good_nodes.add(disease_node)
# all nodes
all_nodes = set([tup[0] for tup in node_freq_tuples_sorted])
# remove the other nodes from the graph
g.remove_nodes_from(all_nodes - good_nodes)
# return the results
if not use_json:
if rev:
to_print = "The most common phenotypes "
else:
to_print = "The least common phenotypes "
to_print += "associated with %s, according to the Columbia Open Health Data, are:\n" % disease_description
for node, freq in node_freq_tuples_sorted_top_n:
to_print += "phenotype: %s\t frequency %f \n" % (
descriptions[node], freq)
print(to_print)
else:
for node, freq in node_freq_tuples_sorted_top_n:
to_print = "According to the Columbia Open Health Data, %s has the phenotype %s with frequency %f." % (
disease_description, descriptions[node], freq)
sub_g = nx.subgraph(g, [disease_node, node])
# add it to the response
response.add_subgraph(sub_g.nodes(data=True),
sub_g.edges(data=True), to_print, freq)
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()
def answer(drug_id,
use_json=False,
num_show=20,
rev=True,
conservative=True):
"""
Answers the question 'what diseases does $drug commonly treat?'
:param disease_id: KG disease node name
:param use_json: bool, use JSON output
:param num_show: int, number to display
:param rev: bool. order by most frequent
:param conservative: bool, True if using exact matches, False if using any synonyms returned by COHD
:return: none
"""
# Initialize the response class
response = FormatOutput.FormatResponse(6)
# get the description
drug_description = RU.get_node_property(drug_id,
'name',
name_type='id')
# Get the conditions that COHD says it's used to treat
conditions_treated = COHDUtilities.get_conditions_treating(
drug_description, conservative=conservative)
# sort the diseases by frequency
ids_counts = []
for id in conditions_treated:
cond = conditions_treated[id]
ids_counts.append((id, cond['concept_count']))
ids_counts_sorted = sorted(ids_counts, key=lambda x: x[1], reverse=rev)
ids_sorted = [i[0] for i in ids_counts_sorted]
# reduce to top n
ids_sorted_top_n = ids_sorted
if len(ids_sorted_top_n) > num_show:
ids_sorted_top_n = ids_sorted_top_n[0:num_show]
# return the results
if not use_json:
if rev:
to_print = "The most common conditions "
else:
to_print = "The least common conditions "
to_print += "treated with %s, according to the Columbia Open Health Data, are:\n" % drug_description
for id in ids_sorted_top_n:
to_print += "condition: %s\t count %d \t frequency %f \n" % (
conditions_treated[id]['associated_concept_name'],
conditions_treated[id]['concept_count'],
conditions_treated[id]['concept_frequency'])
print(to_print)
else:
# otherwise, you want a JSON output
# Attempt to map the COHD names to the KG (this takes some time)l. TODO: find further speed improvements
drug_as_graph = RU.get_node_as_graph(drug_id)
drug_node_info = list(drug_as_graph.nodes(data=True))[0][1]
id_to_KG_name = dict()
id_to_name = dict()
id_to_count = dict()
id_to_frequency = dict()
id_to_id = dict()
# Map ID's to all relevant values
for id in ids_sorted_top_n:
id_to_name[id] = conditions_treated[id][
'associated_concept_name']
id_to_count[id] = conditions_treated[id]['concept_count']
id_to_frequency[id] = conditions_treated[id][
'concept_frequency']
id_to_KG_name[id] = None
try:
id_to_KG_name[id] = RU.get_id_from_property(
id_to_name[id], 'name', label="phenotypic_feature")
id_to_id[id_to_KG_name[id]] = id
except:
try:
id_to_KG_name[id] = RU.get_id_from_property(
id_to_name[id], 'name', label="disease")
id_to_id[id_to_KG_name[id]] = id
except:
try:
id_to_KG_name[id] = RU.get_id_from_property(
id_to_name[id].lower(),
'name',
label="phenotypic_feature")
id_to_id[id_to_KG_name[id]] = id
except:
try:
id_to_KG_name[id] = RU.get_id_from_property(
id_to_name[id].lower(),
'name',
label="disease")
id_to_id[id_to_KG_name[id]] = id
except:
pass
# get the graph (one call) of all the nodes that wer mapped
KG_names = []
for id in ids_sorted_top_n:
if id_to_KG_name[id] is not None:
KG_names.append(id_to_KG_name[id])
if not KG_names:
error_message = "Sorry, Columbia Open Health Data has no data on the use of %s" % drug_description
error_code = "EmptyResult"
response.add_error_message(error_code, error_message)
response.print()
return 1
all_conditions_graph = RU.get_graph_from_nodes(KG_names)
# Get the info of the mapped nodes
id_to_info = dict()
for u, data in all_conditions_graph.nodes(data=True):
id = data['properties']['id']
id = id_to_id[id]
id_to_info[id] = data
# for each condition, return the results (with the nice sub-graph if the cohd id's were mapped)
for id in ids_sorted_top_n:
if id_to_KG_name[id] is not None:
to_print = "According to the Columbia Open Health Data, %s is used to treat patients with the condition %s with frequency " \
"%f out of all patients treated with %s (count=%d)." % (
drug_description, id_to_name[id], id_to_frequency[id], drug_description, id_to_count[id])
nodes = []
disease_node_info = id_to_info[id]
nodes.append((2, disease_node_info))
nodes.append((1, drug_node_info))
edges = [(1, 2, {
'id': 3,
'properties': {
'is_defined_by':
'RTX',
'predicate':
'treats',
'provided_by':
'COHD',
'relation':
'treats',
'seed_node_uuid':
'-1',
'source_node_uuid':
drug_node_info['properties']['UUID'],
'target_node_uuid':
disease_node_info['properties']['UUID']
},
'type': 'treats'
})]
response.add_subgraph(nodes, edges, to_print,
id_to_frequency[id])
else:
to_print = "According to the Columbia Open Health Data, %s is used to treat patients with the condition %s with frequency " \
"%f out of all patients treated with %s (count=%d). This condition is not in our " \
"Knowledge graph, so no graph is shown." % (
drug_description, id_to_name[id], id_to_frequency[id], drug_description, id_to_count[id])
g = RU.get_node_as_graph(drug_id)
response.add_subgraph(g.nodes(data=True),
g.edges(data=True), to_print,
id_to_frequency[id])
response.print()
def answer(tissue_id,
input_protein_list,
use_json=False,
num_show=20,
rev=True):
# Initialize the response class
response = FormatOutput.FormatResponse(6)
# Make sure everything exists in the graph
if not RU.node_exists_with_property(tissue_id, "id"):
tissue_id = RU.get_node_property(tissue_id,
"id",
node_label="anatomical_entity")
for i in range(len(input_protein_list)):
id = input_protein_list[i]
if not RU.node_exists_with_property(id, "id"):
input_protein_list[i] = RU.get_node_property(
id, "id", node_label="protein")
# Initialize the QueryLilGim class
q = QueryLilGIM.QueryLilGIM()
# get the description
tissue_description = RU.get_node_property(
tissue_id, 'name', node_label="anatomical_entity")
# Get the correlated proteins
try:
correlated_proteins_dict = q.query_neighbor_genes_for_gene_set_in_a_given_anatomy(
tissue_id, tuple(input_protein_list))
#correlated_proteins_dict = {'UniProtKB:Q99618': 0.4276333333333333, 'UniProtKB:Q92698': 0.464, 'UniProtKB:P56282': 0.5810000000000001, 'UniProtKB:P49454': 0.4441, 'UniProtKB:P49642': 0.5188333333333334, 'UniProtKB:Q9BZD4': 0.5042666666666668, 'UniProtKB:P38398': 0.4464, 'UniProtKB:Q9BXL8': 0.5009, 'UniProtKB:P42166': 0.4263000000000001, 'UniProtKB:Q96CS2': 0.5844333333333332, 'UniProtKB:Q9BQP7': 0.4903333333333333, 'UniProtKB:O95997': 0.4743333333333333, 'UniProtKB:Q9H4K1': 0.4709, 'UniProtKB:Q9H967': 0.5646666666666667, 'UniProtKB:Q12834': 0.4478, 'UniProtKB:Q71F23': 0.4361, 'UniProtKB:Q9UQ84': 0.4800666666666666, 'UniProtKB:Q9NSP4': 0.4347}
except:
error_message = "Lil'GIM is experiencing a problem."
error_code = "LilGIMerror"
response.add_error_message(error_code, error_message)
response.print()
return 1
# as a list of tuples
correlated_proteins_tupes = []
for k, v in correlated_proteins_dict.items():
correlated_proteins_tupes.append((k, v))
# sort by freq
correlated_proteins_tupes_sorted = sorted(correlated_proteins_tupes,
key=lambda x: x[1],
reverse=rev)
correlated_proteins_tupes_sorted = correlated_proteins_tupes_sorted[
0:num_show]
correlated_proteins_tupes = correlated_proteins_tupes_sorted
# return the results
if not use_json:
try:
protein_descriptions = RU.get_node_property(
input_protein_list[0],
"name",
node_label="protein",
name_type="id")
except:
protein_descriptions = input_protein_list[0]
for id in input_protein_list[1:-1]:
protein_descriptions += ", "
try:
protein_descriptions += RU.get_node_property(
id, "name", node_label="protein", name_type="id")
except:
protein_descriptions += id
if len(input_protein_list) > 1:
try:
protein_descriptions += ", and %s" % RU.get_node_property(
input_protein_list[-1],
"name",
node_label="protein",
name_type="id")
except:
protein_descriptions += ", and %s" % input_protein_list[-1]
if rev:
to_print = "In the tissue: %s, the proteins that correlate most with %s" % (
tissue_description, protein_descriptions)
else:
to_print = "In the tissue: %s, the proteins that correlate least with %s" % (
tissue_description, protein_descriptions)
to_print += " according to Lil'GIM, are:\n"
for id, val in correlated_proteins_tupes_sorted:
try:
to_print += "protein: %s\t correlation %f\n" % (
RU.get_node_property(
id, "name", node_label="protein",
name_type="id"), val)
except:
to_print += "protein: %s\t correlation %f\n" % (id, val)
print(to_print)
else:
# otherwise, you want a JSON output
protein_descriptions = []
is_in_KG_list = []
for protein, corr in correlated_proteins_tupes:
try:
description = RU.get_node_property(protein,
"name",
node_label="protein",
name_type="id")
protein_descriptions.append(description)
is_in_KG_list.append(True)
except:
protein_description = protein
protein_descriptions.append(protein_description)
is_in_KG_list.append(False)
# just get the ones that are actually in the KG. TODO: do something with the ones that are not in the KG
correlated_proteins_tupes_in_KG = []
for i in range(len(correlated_proteins_tupes)):
if is_in_KG_list[i]:
correlated_proteins_tupes_in_KG.append(
correlated_proteins_tupes[i])
# Return the results
full_g = RU.get_graph_from_nodes(
[id for id, val in correlated_proteins_tupes_in_KG],
node_property_label="id")
id2node = dict()
for nx_id, node in full_g.nodes(data=True):
id2node[node['properties']['id']] = node
for id, corr in correlated_proteins_tupes_in_KG:
to_print = "In the tissue: %s, the protein %s has correlation %f with the given list of proteins." % (
tissue_description,
RU.get_node_property(
id, "name", node_label="protein",
name_type="id"), corr)
response.add_subgraph([(id, id2node[id])], [], to_print, corr)
response.print()