def add_subgraph(self, nodes, edges, plain_text, confidence):
"""
Populate the object model using networkx neo4j subgraph
:param nodes: nodes in the subgraph (g.nodes(data=True))
:param edges: edges in the subgraph (g.edges(data=True))
:return: none
"""
# Get the relevant info from the nodes and edges
node_keys = []
node_descriptions = dict()
node_names = dict()
node_labels = dict()
node_uuids = dict()
node_accessions = dict()
node_iris = dict()
node_uuids2iri = dict()
node_curies = dict()
node_uuids2curie = dict()
for u, data in nodes:
node_keys.append(u)
node_descriptions[u] = data['properties']['description']
node_names[u] = data['properties']['name']
node_labels[u] = list(set(data['labels']).difference({'Base'}))[0]
node_uuids[u] = data['properties']['UUID']
node_accessions[u] = data['properties']['accession']
node_iris[u] = data['properties']['iri']
node_uuids2iri[data['properties']
['UUID']] = data['properties']['iri']
node_curies[u] = data['properties']['curie_id']
node_uuids2curie[data['properties']
['UUID']] = data['properties']['curie_id']
edge_keys = []
edge_types = dict()
edge_source_db = dict()
edge_source_iri = dict()
edge_target_iri = dict()
edge_source_curie = dict()
edge_target_curie = dict()
for u, v, data in edges:
edge_keys.append((u, v))
edge_types[(u, v)] = data['type']
edge_source_db[(u, v)] = data['properties']['sourcedb']
edge_source_iri[(
u, v)] = node_uuids2iri[data['properties']['source_node_uuid']]
edge_target_iri[(
u, v)] = node_uuids2iri[data['properties']['target_node_uuid']]
edge_source_curie[(
u,
v)] = node_uuids2curie[data['properties']['source_node_uuid']]
edge_target_curie[(
u,
v)] = node_uuids2curie[data['properties']['target_node_uuid']]
# For each node, populate the relevant information
node_objects = []
node_iris_to_node_object = dict()
for node_key in node_keys:
node = Node()
node.id = node_curies[node_key]
node.type = node_labels[node_key]
node.name = node_names[node_key]
node.accession = node_accessions[node_key]
node.description = node_descriptions[node_key]
node_objects.append(node)
node_iris_to_node_object[node_iris[node_key]] = node
# for each edge, create an edge between them
edge_objects = []
for u, v in edge_keys:
edge = Edge()
edge.type = edge_types[(u, v)]
edge.source_id = node_iris_to_node_object[edge_source_iri[(u,
v)]].id
edge.target_id = node_iris_to_node_object[edge_target_iri[(u,
v)]].id
edge.origin_list = []
edge.origin_list.append(
edge_source_db[(u, v)]
) # TODO: check with eric if this really should be a list and if it should contain the source DB('s)
edge_objects.append(edge)
# Create the result (potential answer)
result1 = Result()
#result1.id = "http://rtx.ncats.io/api/v1/response/1234/result/2345"
#result1.id = "-1"
result1.text = plain_text
result1.confidence = confidence
# Create a ResultGraph object and put the list of nodes and edges into it
result_graph = ResultGraph()
result_graph.node_list = node_objects
result_graph.edge_list = edge_objects
# Put the ResultGraph into the first result (potential answer)
result1.result_graph = result_graph
# Put the first result (potential answer) into the response
self._result_list.append(result1)
self.response.result_list = self._result_list
# Increment the number of results
self._num_results += 1
if self._num_results == 1:
self.response.message = "%s result found" % self._num_results
else:
self.response.message = "%s results found" % self._num_results
def test1(self):
#### Create the response object and fill it with attributes about the response
response = Response()
response.context = "http://translator.ncats.io"
response.id = "http://rtx.ncats.io/api/v1/response/1234"
response.type = "medical_translator_query_response"
response.tool_version = "RTX 0.4"
response.schema_version = "0.5"
response.datetime = datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S")
response.original_question_text = "what proteins are affected by sickle cell anemia"
response.restated_question_text = "Which proteins are affected by sickle cell anemia?"
response.result_code = "OK"
response.message = "1 result found"
#### Create a disease node
node1 = Node()
node1.id = "http://omim.org/entry/603903"
node1.type = "disease"
node1.name = "sickle cell anemia"
node1.accession = "OMIM:603903"
node1.description = "A disease characterized by chronic hemolytic anemia..."
#### Create a protein node
node2 = Node()
node2.id = "https://www.uniprot.org/uniprot/P00738"
node2.type = "protein"
node2.name = "Haptoglobin"
node2.symbol = "HP"
node2.accession = "UNIPROT:P00738"
node2.description = "Haptoglobin captures, and combines with free plasma hemoglobin..."
#### Create a node attribute
node2attribute1 = NodeAttribute()
node2attribute1.type = "comment"
node2attribute1.name = "Complex_description"
node2attribute1.value = "The Hemoglobin/haptoglobin complex is composed of a haptoglobin dimer bound to two hemoglobin alpha-beta dimers"
node2.node_attributes = [node2attribute1]
#### Create an edge between these 2 nodes
edge1 = Edge()
edge1.type = "is_caused_by_a_defect_in"
edge1.source_id = node1.id
edge1.target_id = node2.id
edge1.confidence = 1.0
#### Add an origin and property for the edge
origin1 = Origin()
origin1.id = "https://api.monarchinitiative.org/api/bioentity/disease/OMIM:603903/genes/"
origin1.type = "Monarch_BioLink_API_Relationship"
#### Add an attribute
attribute1 = EdgeAttribute()
attribute1.type = "PubMed_article"
attribute1.name = "Orthopaedic Manifestations of Sickle Cell Disease"
attribute1.value = None
attribute1.url = "https://www.ncbi.nlm.nih.gov/pubmed/29309293"
origin1.attribute_list = [attribute1]
edge1.origin_list = [origin1]
#### Create the first result (potential answer)
result1 = Result()
result1.id = "http://rtx.ncats.io/api/v1/response/1234/result/2345"
result1.text = "A free text description of this result"
result1.confidence = 0.932
#### Create a ResultGraph object and put the list of nodes and edges into it
result_graph = ResultGraph()
result_graph.node_list = [node1, node2]
result_graph.edge_list = [edge1]
#### Put the ResultGraph into the first result (potential answer)
result1.result_graph = result_graph
#### Put the first result (potential answer) into the response
result_list = [result1]
response.result_list = result_list
print(response)