def _extract_chp_query(self, query, message_type):
# Extract Message
message = query.message
# Initialize CHP BKB Query
chp_query = ChpQuery(reasoning_type='updating')
# Grab edge
for qedge_id, qedge in message.query_graph.edges.items():
break
# Process predicate proxy
chp_query = self._process_predicate_proxy(qedge, chp_query)
# Process predicate context
chp_query = self._process_predicate_context(qedge, message_type,
chp_query)
#TODO: Probably need a more robust solution for when no context is provided in wildcard queries and you need it.
#if len(evidence) == 0:
# raise ValueError('Did not supply context with a query that required context.')
if message_type == 'standard':
# Setup gene and drug evidence
for qnode_id, qnode in message.query_graph.nodes.items():
if qnode.categories[
0] == BIOLINK_GENE_ENTITY or qnode.categories[
0] == BIOLINK_DRUG_ENTITY:
chp_query.add_meta_evidence(qnode.ids[0], 'True')
elif message_type == 'gene' or message_type == 'drug_two_hop':
for qnode_id, qnode in message.query_graph.nodes.items():
if qnode.categories[0] == BIOLINK_DRUG_ENTITY:
if qnode.ids is not None:
chp_query.add_meta_evidence(qnode.ids[0], 'True')
elif message_type == 'drug' or message_type == 'gene_two_hop':
for qnode_id, qnode in message.query_graph.nodes.items():
if qnode.categories[0] == BIOLINK_GENE_ENTITY:
if qnode.ids is not None:
chp_query.add_meta_evidence(qnode.ids[0], 'True')
target = list(chp_query.dynamic_targets.keys())[0]
truth_target = (target, '{} {}'.format(
chp_query.dynamic_targets[target]["op"],
chp_query.dynamic_targets[target]["value"]))
# Set some other helpful attributes
chp_query.truth_target = truth_target
return chp_query
def _extract_chp_query(self, message, message_type=None):
# Initialize Chp Query
chp_query = ChpQuery(reasoning_type='updating')
# Ensure we are using all nodes/edges
total_nodes = 0
total_edges = 0
query_graph = message.query_graph
# get phenotype node
targets = list()
for node_key in query_graph.nodes.keys():
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_PHENOTYPIC_FEATURE_ENTITY:
target_id = node_key
total_nodes += 1
survival_value = 970
survival_operator = '>='
# get disease node info and ensure only 1 disease:
for node_key in query_graph.nodes.keys():
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_DISEASE_ENTITY:
disease_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(
edge.predicates[0], BIOLINK_HAS_PHENOTYPE_ENTITY
) and edge.subject == disease_id and edge.object == target_id:
survival_time_constraint = edge.find_constraint(
name='survival_time')
if survival_time_constraint is not None:
survival_value = survival_time_constraint.value
survival_operator = survival_time_constraint.operator
if survival_operator == 'matches':
survival_operator = '=='
total_edges += 1
total_nodes += 1
# set BKB target
chp_query.add_dynamic_target(node.ids[0], survival_operator,
survival_value)
truth_target = (node.ids[0], '{} {}'.format(survival_operator,
survival_value))
# get evidence
for node_key in query_graph.nodes.keys():
# genes
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_GENE_ENTITY:
# check for appropriate gene node structure
gene_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(
edge.predicates[0],
BIOLINK_GENE_ASSOCIATED_WITH_CONDITION_ENTITY
) and edge.subject == gene_id and edge.object == disease_id:
total_edges += 1
# check for appropriate gene node curie
gene_curie = node.ids[0]
gene = gene_curie
chp_query.add_meta_evidence(gene, 'True')
total_nodes += 1
# drugs
if node.categories[0] == BIOLINK_DRUG_ENTITY:
# check for appropriate drug node structure
drug_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(
edge.predicates[0], BIOLINK_TREATS_ENTITY
) and edge.subject == drug_id and edge.object == disease_id:
total_edges += 1
# check for appropriate drug node curie
drug_curie = node.ids[0]
drug = drug_curie
chp_query.add_dynamic_evidence(node.ids[0], '==', 'True')
total_nodes += 1
# Set some other helpful attributes
chp_query.truth_target = truth_target
return chp_query
def _extract_chp_query(self, message, message_type):
# Initialize CHP BKB Query
chp_query = ChpQuery(reasoning_type='updating')
# ensure we are using all nodes/edges
total_nodes = 0
total_edges = 0
query_graph = message.query_graph
# get phenotype node
targets = list()
acceptable_target_curies = ['EFO:0000714']
self.implicit_survival_node = False
for node_key in query_graph.nodes.keys():
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_PHENOTYPIC_FEATURE_ENTITY and node.ids[0] in acceptable_target_curies:
target_id = node_key
total_nodes += 1
if total_nodes == 0:
# Use Default Survival
self.implicit_survival_node = True
total_nodes += 1
#acceptable_target_curies_print = ','.join(acceptable_target_curies)
#sys.exit("Survival Node not found. Node category must be '{}' and id must be in: {}".format(Biolink(BIOLINK_PHENOTYPIC_FEATURE),
# acceptable_target_curies_print))
survival_value = 970
survival_operator = '>='
# get disease node info and ensure only 1 disease:
acceptable_disease_curies = ['MONDO:0007254']
for node_key in query_graph.nodes.keys():
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_DISEASE_ENTITY and node.ids[0] in acceptable_disease_curies:
disease_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(edge.predicates[0], BIOLINK_HAS_PHENOTYPE_ENTITY) and edge.subject == disease_id and edge.object == target_id:
survival_time_constraint = edge.find_constraint(name='survival_time')
if survival_time_constraint is not None:
survival_value = survival_time_constraint.value
survival_operator = survival_time_constraint.operator
if survival_operator == 'matches':
survival_operator = '=='
total_edges += 1
total_nodes += 1
if self.implicit_survival_node:
days=970
qualifier = '>='
total_edges += 1
# set BKB target
chp_query.add_dynamic_target('EFO:0000714', survival_operator, survival_value)
truth_target = ('EFO:0000714', '{} {}'.format(survival_operator, survival_value))
# get evidence
for node_key in query_graph.nodes.keys():
# genes
node = query_graph.nodes[node_key]
if node.categories[0] == BIOLINK_GENE_ENTITY:
# check for appropriate gene node structure
gene_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(edge.predicates[0], BIOLINK_GENE_ASSOCIATED_WITH_CONDITION_ENTITY) and edge.subject == gene_id and edge.object == disease_id:
total_edges += 1
# check for appropriate gene node curie
if message_type != 'gene':
gene_curie = node.ids[0]
if gene_curie in self.curies[BIOLINK_GENE_ENTITY.get_curie()]:
gene = gene_curie
chp_query.add_meta_evidence(gene, 'True')
total_nodes += 1
# drugs
if node.categories[0] == BIOLINK_DRUG_ENTITY:
# check for appropriate drug node structure
drug_id = node_key
for edge_key in query_graph.edges.keys():
edge = query_graph.edges[edge_key]
if self.check_predicate_support(edge.predicates[0], BIOLINK_TREATS_ENTITY) and edge.subject == drug_id and edge.object == disease_id:
total_edges += 1
# check for appropriate drug node curie
if message_type != 'drug':
drug_curie = node.ids[0]
if drug_curie in self.curies[BIOLINK_DRUG_ENTITY.get_curie()]:
drug = drug_curie
chp_query.add_meta_evidence(drug, 'True')
total_nodes += 1
# Temporary solution to no evidence linking
if len(chp_query.evidence.keys()) == 0 and len(chp_query.dynamic_evidence.keys()) == 0:
self.no_evidence_probability_check = True
else:
self.no_evidence_probability_check = False
# Set some other helpful attributes
chp_query.truth_target = truth_target
return chp_query
