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 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
