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(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(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()