Nodes: {net.num_nodes()} Edges: {net.num_edges()}
" - net.show(f"{predicate}.html") - return dic_graph \ No newline at end of file + if output_filename_prefix is None: + net.show(f"{predicate}.html") + else: + net.show(f"{output_filename_prefix}{predicate}.html") + return dic_graph diff --git a/TCT/TCT_neighborhood_finder.py b/TCT/TCT_neighborhood_finder.py new file mode 100644 index 0000000..bdef854 --- /dev/null +++ b/TCT/TCT_neighborhood_finder.py @@ -0,0 +1,376 @@ +from collections import Counter + +from .TCT import sele_predicates_API, parse_KG, rank_by_primary_infores +from .TCT_pathfinder import generate_score_results, build_query_graph +from .translator_query import format_query_json + +def parse_results_for_neighborhood_finder(start_node_id:str, results:dict, + start_node_categories=None, end_node_categories=None, + get_node_info=True, + scoring_method='infores'): + """ + Converts the results of two TRAPI queries into the same general json format as the other pathfinder APIs. + scoring_method is how the node scores are generated, and could be 'infores' or 'edges'. + """ + # nodes + node_info = {} + # edges is a dict of intermediate nodes + node_edges = {} + for k, v in results.items(): + i1 = v['subject'] + i2 = v['object'] + s_o = 'object' + if i1 == start_node_id: + intermediate_node_id = i2 + s_o = 'object' + elif i2 == start_node_id: + intermediate_node_id = i1 + s_o = 'subject' + else: + continue + if (i1 == start_node_id or i2 == start_node_id) and intermediate_node_id in node_edges: + node_edges[intermediate_node_id].append((k, v)) + else: + node_edges[intermediate_node_id] = [(k, v)] + # add node dict + if intermediate_node_id not in node_info: + node_dict = { + } + node_info[intermediate_node_id] = node_dict + else: + node_dict = node_info[intermediate_node_id] + if 'attributes' not in v: + v['attributes'] = [] + for attribute in v['attributes']: + if attribute['attribute_type_id'] == f'{s_o}_category': + if 'categories' not in node_dict: + node_dict['categories'] = set([attribute['value']]) + else: + node_dict['categories'].add(attribute['value']) + if attribute['attribute_type_id'] == f'{s_o}_name' and 'name' not in node_dict: + node_dict['name'] = attribute['value'] + node_info[intermediate_node_id] = node_dict + for k, v in node_info.items(): + if 'categories' in v: + v['categories'] = list(v['categories']) + all_edges = {} + all_auxiliary_graphs = {} + i = 1 + # sort connecting_intermediate_nodes by total number of connections + connection_counts = Counter({k: len(v) for k, v in node_edges.items()}) + for i1, count in connection_counts.most_common(): + edges = node_edges[i1] + all_edges.update({k: v for k, v in edges}) + keys = [x[0] for x in edges] + all_auxiliary_graphs[f'aux_{i}_{i1}'] = keys + i += 1 + # generate output json + output = { + 'query_graph': build_query_graph(start_node_id, '', start_node_categories, end_node_categories), + 'knowledge_graph': {'nodes': {x: node_info[x] for x in connection_counts.keys()}, + 'edges': all_edges, + }, + 'results': [{'analyses': []}], + 'auxiliary_graphs': all_auxiliary_graphs + } + graph_scores, graph_scores_formatted = generate_score_results(output, method=scoring_method) + output['results'][0]['analyses'] = graph_scores_formatted + if get_node_info: + from .node_normalizer import get_normalized_nodes + nodes_to_add = [] + for k, v in output['knowledge_graph']['nodes'].items(): + if 'name' not in v or 'categories' not in v: + nodes_to_add.append(k) + if nodes_to_add: + batch_limit = 1000 + all_normalized_nodes = {} + for idx in range(0, len(nodes_to_add), batch_limit): + batch = nodes_to_add[idx:idx + batch_limit] + batch_result = get_normalized_nodes(batch, mode='post') + all_normalized_nodes.update(batch_result) + for node_id in nodes_to_add: + nn = all_normalized_nodes.get(node_id) + if nn is not None: + output['knowledge_graph']['nodes'][node_id] = {'name': nn.label, 'categories': nn.types} + return output + + +# TODO: can we use multiple input nodes? +def parse_results_for_neighborhood_finder_multiple_inputs(start_node_ids:list[str], results:dict, + start_node_categories=None, end_node_categories=None, + get_node_info=True, + scoring_method='infores'): + """ + Converts the results of two TRAPI queries into the same general json format as the other pathfinder APIs. + scoring_method is how the node scores are generated, and could be 'infores' or 'edges'. + """ + # nodes + node_info = {} + # edges is a dict of intermediate nodes + node_edges = {} + for k, v in results.items(): + i1 = v['subject'] + i2 = v['object'] + s_o = 'object' + if i1 in start_node_ids: + intermediate_node_id = i2 + s_o = 'object' + elif i2 in start_node_ids: + intermediate_node_id = i1 + s_o = 'subject' + else: + continue + if (i1 in start_node_ids or i2 in start_node_ids) and intermediate_node_id in node_edges: + node_edges[intermediate_node_id].append((k, v)) + else: + node_edges[intermediate_node_id] = [(k, v)] + # add node dict + if intermediate_node_id not in node_info: + node_dict = { + } + node_info[intermediate_node_id] = node_dict + else: + node_dict = node_info[intermediate_node_id] + if 'attributes' not in v: + v['attributes'] = [] + for attribute in v['attributes']: + if attribute['attribute_type_id'] == f'{s_o}_category': + if 'categories' not in node_dict: + node_dict['categories'] = set([attribute['value']]) + else: + node_dict['categories'].add(attribute['value']) + if attribute['attribute_type_id'] == f'{s_o}_name' and 'name' not in node_dict: + node_dict['name'] = attribute['value'] + node_info[intermediate_node_id] = node_dict + for k, v in node_info.items(): + if 'categories' in v: + v['categories'] = list(v['categories']) + all_edges = {} + all_auxiliary_graphs = {} + i = 1 + # sort connecting_intermediate_nodes by total number of connections + connection_counts = Counter({k: len(v) for k, v in node_edges.items()}) + for i1, count in connection_counts.most_common(): + edges = node_edges[i1] + all_edges.update({k: v for k, v in edges}) + keys = [x[0] for x in edges] + all_auxiliary_graphs[f'aux_{i}_{i1}'] = keys + i += 1 + # generate output json + output = { + 'query_graph': build_query_graph(start_node_ids, '', start_node_categories, end_node_categories), + 'knowledge_graph': {'nodes': {x: node_info[x] for x in connection_counts.keys()}, + 'edges': all_edges, + }, + 'results': [{'analyses': []}], + 'auxiliary_graphs': all_auxiliary_graphs + } + graph_scores, graph_scores_formatted = generate_score_results(output, method=scoring_method) + output['results'][0]['analyses'] = graph_scores_formatted + if get_node_info: + from .node_normalizer import get_normalized_nodes + nodes_to_add = [] + for k, v in output['knowledge_graph']['nodes'].items(): + if 'name' not in v or 'categories' not in v: + nodes_to_add.append(k) + if nodes_to_add: + batch_limit = 1000 + all_normalized_nodes = {} + for idx in range(0, len(nodes_to_add), batch_limit): + batch = nodes_to_add[idx:idx + batch_limit] + batch_result = get_normalized_nodes(batch, mode='post') + all_normalized_nodes.update(batch_result) + for node_id in nodes_to_add: + nn = all_normalized_nodes.get(node_id) + if nn is not None: + output['knowledge_graph']['nodes'][node_id] = {'name': nn.label, 'categories': nn.types} + return output + +def neighborhood_finder(input_node, node2_categories, APInames, metaKG, API_predicates, + input_node_category = [], + predicates_subset=None, + attribute_constraints=None): + """ + This function is used to find the neighborhood of a given input node with intermediate categories. + + Parameters + ---------- + input_node (str) + The input node - should be a CURIE id. + node2_categories (list) + A list of intermediate categories to be used in the neighborhood finding process. + APInames (dict) + A dictionary containing the names of the APIs to be used. + metaKG (DataFrame) + The metadata knowledge graph containing information about the APIs and their predicates. + API_predicates (dict) + A dictionary containing the predicates for each API. + input_node_category (list) + Optional. A list of categories for the input node. If empty, it will be derived from the input node's types. + attribute_constraints (list) + Optional. List of outputs of translator_query.build_attribute_constraint + + Returns + -------------- + input_node_id (str) + The curie id of the input node. + result (dict) + The result of the query for the input node. + result_parsed (DataFrame) + The parsed results for the input node. + result_ranked_by_primary_infores (DataFrame) + The ranked results based on primary infores. + + -------------- + Example: + >>> input_node_id, result, result_parsed, result_ranked_by_primary_infores1 = neighborhood_finder('MONDO:0008170', #Ovarian Cancer + node2_categories = ['biolink:SmallMolecule', 'biolink:Drug', 'biolink:ChemicalEntity'], + APInames = APInames, + metaKG = metaKG, + API_predicates = API_predicates) + -------------- + + """ + from . import node_normalizer + from . import translator_query + + input_node_id = input_node + # Step 1: Resolve the input node to get its curie id and categories + input_node_info = node_normalizer.get_normalized_nodes(input_node_id) + print(input_node_id) + + if len(input_node_category) == 0: + input_node_category = input_node_info.types + else: + input_node_category = list(set(input_node_category).intersection(set(input_node_info.types))) + if len(input_node_category) == 0: + input_node_category = input_node_info.types + + # Step 2: Select predicates and APIs based on the intermediate categories + sele_predicates, sele_APIs, API_URLs = sele_predicates_API(input_node_category, + node2_categories, + metaKG, APInames) + + if len(sele_predicates) ==0: + sele_predicates = ["biolink:related_to"] + + # Step 3: Format the query JSON for the input node + #query_json = format_query_json([input_node_id], + # None, + # None, + # node2_categories, + # sele_predicates, + # attribute_constraints=attribute_constraints) + query_json = format_query_json(subject_ids = [input_node_id], + object_ids = None, + subject_categories = None, + object_categories = node2_categories, + predicates = sele_predicates, + attribute_constraints = None, + ) + # Step 4: Query the APIs in parallel + result = translator_query.parallel_api_query(query_json=query_json, + select_APIs= sele_APIs, + APInames=APInames, + API_predicates=API_predicates, + max_workers=len(sele_APIs)) + result_parsed = parse_KG(result) + # Step 7: Ranking the results. This ranking method is based on the number of unique + # primary infores. It can only be used to rank the results with one defined node. + result_ranked_by_primary_infores1 = rank_by_primary_infores(result_parsed, input_node_id) # input_node1_id is the curie id of the + parsed_results = parse_results_for_neighborhood_finder(input_node_id, result, input_node_category, node2_categories) + return input_node_id, result, parsed_results, result_ranked_by_primary_infores1 + + +def neighborhood_finder_multiple_inputs(input_nodes:list[str], node2_categories:list[str], APInames, metaKG, API_predicates, + input_node_category = [], + predicates_subset=None, + attribute_constraints=None): + """ + This function is used to find the neighborhood of a given input node with intermediate categories. + + Parameters + ---------- + input_nodes (list[str]) + The input nodes - should be a list of CURIE ids. + node2_categories (list) + A list of intermediate categories to be used in the neighborhood finding process. + APInames (dict) + A dictionary containing the names of the APIs to be used. + metaKG (DataFrame) + The metadata knowledge graph containing information about the APIs and their predicates. + API_predicates (dict) + A dictionary containing the predicates for each API. + input_node_category (list) + Optional. A list of categories for the input node. If empty, it will be derived from the input node's types. + attribute_constraints (list) + Optional. List of outputs of translator_query.build_attribute_constraint + + Returns + -------------- + input_node_id (str) + The curie id of the input node. + result (dict) + The result of the query for the input node. + result_parsed (DataFrame) + The parsed results for the input node. + result_ranked_by_primary_infores (DataFrame) + The ranked results based on primary infores. + + -------------- + Example: + >>> input_node_id, result, result_parsed, result_ranked_by_primary_infores1 = neighborhood_finder('MONDO:0008170', #Ovarian Cancer + node2_categories = ['biolink:SmallMolecule', 'biolink:Drug', 'biolink:ChemicalEntity'], + APInames = APInames, + metaKG = metaKG, + API_predicates = API_predicates) + -------------- + + """ + from . import node_normalizer + from . import translator_query + + # Step 1: Resolve the input nodes to get its curie id and categories + input_nodes_info = node_normalizer.get_normalized_nodes(input_nodes) + print(input_nodes) + + if len(input_node_category) == 0: + input_node_category = input_nodes_info[input_nodes[0]].types + else: + input_node_info = input_nodes_info[input_nodes[0]] + input_node_category = list(set(input_node_category).intersection(set(input_node_info.types))) + if len(input_node_category) == 0: + input_node_category = input_node_info.types + + # Step 2: Select predicates and APIs based on the intermediate categories + sele_predicates, sele_APIs, API_URLs = sele_predicates_API(input_node_category, + node2_categories, + metaKG, APInames) + + if len(sele_predicates) ==0: + sele_predicates = ["biolink:related_to"] + + # Step 3: Format the query JSON for the input node + #query_json = format_query_json([input_node_id], + # None, + # None, + # node2_categories, + # sele_predicates, + # attribute_constraints=attribute_constraints) + query_json = format_query_json(subject_ids = input_nodes, + object_ids = None, + subject_categories = None, + object_categories = node2_categories, + predicates = sele_predicates, + attribute_constraints = attribute_constraints, + ) + # Step 4: Query the APIs in parallel + result = translator_query.parallel_api_query(query_json=query_json, + select_APIs= sele_APIs, + APInames=APInames, + API_predicates=API_predicates, + max_workers=len(sele_APIs)) + parsed_results = parse_results_for_neighborhood_finder_multiple_inputs(input_nodes, result, input_node_category, node2_categories) + return result, parsed_results + + diff --git a/TCT/TCT_network_annotator.py b/TCT/TCT_network_annotator.py new file mode 100644 index 0000000..ce012c1 --- /dev/null +++ b/TCT/TCT_network_annotator.py @@ -0,0 +1,272 @@ +import json + +from . import node_normalizer +from . import translator_query +from . import name_resolver +from . import TCT_neighborhood_finder + + +def network_annotator(gene_list, + select_APIs, + node2_categories, + select_metaKG, + API_predicates, + output_file=None): + """ + Filter multiple TRAPI neighborhood JSON files by gene list and merge + them into a single TRAPI JSON. + + Parameters + ---------- + gene_list : list + Gene symbols to keep. + output_file : str, optional + Output merged JSON file. + + Returns + ------- + dict + Merged TRAPI JSON object. + """ + import json + result = {} + TCT_neighborhood_finder_result = {} + for gene in gene_list: + input_identifiers = name_resolver.lookup(gene, only_taxa='NCBITaxon:9606', biolink_type='biolink:Gene').curie + + input_node_id, result[input_identifiers], result_parsed, result_ranked_by_primary_infores = TCT_neighborhood_finder.neighborhood_finder(input_identifiers, + node2_categories = node2_categories, + APInames = select_APIs, + metaKG = select_metaKG, + API_predicates = API_predicates) + + + for input_identifiers in result.keys(): + TCT_neighborhood_finder_result[input_identifiers] = TCT_neighborhood_finder.parse_results_for_neighborhood_finder(input_identifiers, result[input_identifiers], + start_node_categories='biolink:Gene', end_node_categories=None, + get_node_info=True, + scoring_method='infores') + + gene_set = set(gene_list) + + merged = { + "query_graph": None, + "knowledge_graph": { + "nodes": {}, + "edges": {} + }, + "results": [] + } + + for json_cur in TCT_neighborhood_finder_result.values(): + + data = json_cur + + # Keep first query graph + if merged["query_graph"] is None: + merged["query_graph"] = data.get("query_graph", {}) + + kg_nodes = data.get("knowledge_graph", {}).get("nodes", {}) + kg_edges = data.get("knowledge_graph", {}).get("edges", {}) + + # -------------------------------------------------- + # Step 1: keep gene nodes in gene_list + # -------------------------------------------------- + keep_node_ids = { + node_id + for node_id, node_info in kg_nodes.items() + if node_info.get("name") in gene_set + } + + # -------------------------------------------------- + # Step 2: keep edges connecting retained nodes + # -------------------------------------------------- + keep_edge_ids = set() + + for edge_id, edge_info in kg_edges.items(): + + subject = edge_info.get("subject") + obj = edge_info.get("object") + + if subject in keep_node_ids and obj in keep_node_ids: + keep_edge_ids.add(edge_id) + + # -------------------------------------------------- + # Step 3: build filtered node/edge dictionaries + # -------------------------------------------------- + filtered_nodes = { + node_id: kg_nodes[node_id] + for node_id in keep_node_ids + } + + filtered_edges = { + edge_id: kg_edges[edge_id] + for edge_id in keep_edge_ids + } + + # -------------------------------------------------- + # Step 4: filter results + # -------------------------------------------------- + filtered_results = [] + + for result in data.get("results", []): + + keep_result = True + + # node bindings + for bindings in result.get("node_bindings", {}).values(): + + for binding in bindings: + + if binding["id"] not in keep_node_ids: + keep_result = False + break + + if not keep_result: + break + + # edge bindings + if keep_result: + + for analysis in result.get("analyses", []): + + for bindings in analysis.get( + "edge_bindings", {} + ).values(): + + for binding in bindings: + + if binding["id"] not in keep_edge_ids: + keep_result = False + break + + if not keep_result: + break + + if not keep_result: + break + + if keep_result: + filtered_results.append(result) + + # -------------------------------------------------- + # Step 5: merge + # -------------------------------------------------- + merged["knowledge_graph"]["nodes"].update(filtered_nodes) + merged["knowledge_graph"]["edges"].update(filtered_edges) + merged["results"].extend(filtered_results) + + # ------------------------------------------------------ + # Step 6: save merged file + # ------------------------------------------------------ + if output_file: + + with open(output_file, "w") as f: + json.dump(merged, f, indent=2) + + return merged + + +def get_connected_graph(result_parsed, input_identifiers): + """ + This function is used to extract the connected graph from the returned graph based on neiborhood finder from multiple inputs. + It will only include the input nodes and the nodes that are connected to the input nodes with degree greater than 1. + + + Parameters + ---------- + result_parsed (dict) + The parsed results for results from TCT_neighborhood_finder.neighborhood_finder_multiple_inputs(). + input_identifiers (list) + A list of input node identifiers, which are also input for TCT_neighborhood_finder.neighborhood_finder_multiple_inputs(). + + Returns + -------------- + result_parsed (dict) + The connected graph extracted from the parsed results. + + -------------- + Example: + >>> + input_identifiers = ['NCBIGene:6774', 'NCBIGene:4170','NCBIGene:4792','NCBIGene:4288','NCBIGene:596','NCBIGene:581','NCBIGene:836','NCBIGene:6777','NCBIGene:4790','NCBIGene:545'] + + result, result_parsed = TCT_neighborhood_finder.neighborhood_finder_multiple_inputs(input_identifiers, + node2_categories = ['biolink:Gene','biolink:Protein'], + APInames = APInames, + metaKG = metaKG, + API_predicates = API_predicates) + + result = TCT_network_annotator.get_connected_graph(result_parsed, input_identifiers) + import datetime + import json + timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S') + with open('./PathFinder_testing_results/'+'TCT_neighborhood_finder_result_'+'_'+timestamp+'.json', 'w') as f: + json.dump(result, f) + # visualize the result using the TCT visualization tool (https://github.com/NCATSTranslator/Translator_component_toolkit/blob/main/notebooks/visulize_path_finder_results.html). + -------------- + + """ + + result_parsed_copy = result_parsed.copy() + # count node degrees based on edge in the result_parsed['knowledge_graph']['edges'], if multiple nodes are connecting to the same node, then count the degree of that node as the number of nodes connecting to it. If there is only one node connecting to it, then count the degree of that node as 1. + + result_parsed_copy = result_parsed.copy() + # count node degrees based on edge in the result_parsed['knowledge_graph']['edges'], if multiple nodes are connecting to the same node, then count the degree of that node as the number of nodes connecting to it. If there is only one node connecting to it, then count the degree of that node as 1. + + dic_connected_nodes = {} + for key in result_parsed_copy['knowledge_graph']['edges']: + + edge = result_parsed_copy['knowledge_graph']['edges'][key] + subject = edge['subject'] + object = edge['object'] + + if subject not in dic_connected_nodes: + dic_connected_nodes[subject] = set() + if object not in dic_connected_nodes: + dic_connected_nodes[object] = set() + dic_connected_nodes[subject].add(object) + dic_connected_nodes[object].add(subject) + + # count the degree of each node based on the dic_connected_nodes + node_degrees = {} + for node in dic_connected_nodes: + node_degrees[node] = len(dic_connected_nodes[node]) + + + # select the nodes if the degree of the node is greater than 1 or the node is in the input_identifiers + selected_nodes = [] + for node in node_degrees: + if node_degrees[node] > 1 or node in input_identifiers: + selected_nodes.append(node) + + # get the edges if the edge connects to the selected nodes + selected_edges_id = [] + selected_edges = {} + for key in result_parsed_copy['knowledge_graph']['edges']: + edge = result_parsed_copy['knowledge_graph']['edges'][key] + subject = edge['subject'] + object = edge['object'] + if subject in selected_nodes and object in selected_nodes: + selected_edges_id.append(key) + selected_edges[key] = edge + + #remove the result_parsed['auxiliary_graphs'] if the selected_edges_id is in the result_parsed['auxiliary_graphs'] + for key in list(result_parsed_copy['auxiliary_graphs'].keys()): + current_auxiliary_graph = result_parsed_copy['auxiliary_graphs'][key] + # if all current_auxiliary_graph was included in selected_edges_id, then keep it, otherwise remove it + if len(current_auxiliary_graph) == len(set(current_auxiliary_graph).intersection(set(selected_edges_id))): + continue + else: + del result_parsed_copy['auxiliary_graphs'][key] + + # remove the edges in result_parsed_copy['knowledge_graph']['edges'] that are not in selected_edges_id + for key in list(result_parsed_copy['knowledge_graph']['edges'].keys()): + if key not in selected_edges_id: + del result_parsed_copy['knowledge_graph']['edges'][key] + + # remove the nodes in result_parsed_copy['knowledge_graph']['nodes'] that are not in selected_nodes + for key in list(result_parsed_copy['knowledge_graph']['nodes'].keys()): + if key not in selected_nodes: + del result_parsed_copy['knowledge_graph']['nodes'][key] + + return result_parsed_copy \ No newline at end of file diff --git a/TCT/TCT_pathfinder.py b/TCT/TCT_pathfinder.py new file mode 100644 index 0000000..2a0dd6f --- /dev/null +++ b/TCT/TCT_pathfinder.py @@ -0,0 +1,433 @@ +# TCT Pathfinder... +import requests + +from collections import Counter + +from . import node_normalizer +from . import translator_query +from .TCT import sele_predicates_API + +def format_query_json_for_pathfinder_with_constraints(subject_ids, + object_ids=None, + subject_categories=None, + object_categories=None, + predicates=None, + constraints=None + ): + """ + format user's input into a query json for pathfinder pipeline with constraints on the intermediate node categories. + + Parameters + ---------- + subject_ids : str + a curie id for the subject node + object_ids : str + a curie id for the object node + subject_categories : list + a list of categories for the subject node + object_categories : list + a list of categories for the object node + predicates : list + a list of predicates for the edge between subject and object nodes + constraints : list + a list of intermediate categories for the pathfinder pipeline, currently only one intermediate category is allowed in the constraints list. + Returns + ------- + query_json_temp : dict + a query json for pathfinder pipeline + + Example + ------- + query_json_temp = format_query_json_for_pathfinder_with_constraints( + subject_ids='NCBIGene:6774', + object_ids='NCBIGene:4170', + subject_categories=['biolink:Gene'], + object_categories=['biolink:Gene'], + predicates=['biolink:related_to'], + constraints=['biolink:Protein'] + """ + if constraints is None or len(constraints) == 0: + constraints_intermediate_category = None + if len(constraints) == 1: + constraints_intermediate_category = constraints + + else: + constraints_intermediate_category = [constraints[0]] + print("Warning: for ARAGORN or ARAX pathfinder pipeline, it is only allowed to have only one intermediate category in the constraints list. If there are multiple intermediate categories, the query will return an error. Therefore, we will only use one intermediate category in the constraints list. ") + q = { + "message": { + "query_graph": { + "nodes": { + "n0": { + "ids": [ + subject_ids + ] + }, + "n1": { + "ids": [ + object_ids + ] + } + }, + "paths": { + "p0": { + "subject": "n0", + "object": "n1", + #"predicates": [ + # "biolink:related_to" + #], + "constraints": [ + { + "intermediate_categories": constraints_intermediate_category + } + ] + } + } + } + }, + "submitter": "TCT", + #"stream_progress": True, + "query_options": { + "kp_timeout": "30", + "prune_threshold": "50", + "max_pathfinder_paths": "500", + "max_path_length": 4 + } + } + + return q + +def build_query_graph(start_node_id, end_node_id, start_node_categories=None, end_node_categories=None, constraints_path=None): + """ + start_node_categories and end_node_categories are lists of categories. + """ + q = { + "nodes": { + "on": { + "categories": end_node_categories, + "constraints": [], + "ids": [ + end_node_id + ], + "is_set": False, + "option_group_id": None, + "set_id": None, + "set_interpretation": "BATCH" + }, + "sn": { + "categories": start_node_categories, + "constraints": [], + "ids": [start_node_id] if isinstance(start_node_id, str) else start_node_id, + "is_set": False, + "option_group_id": None, + "set_id": None, + "set_interpretation": "BATCH" + } + }, + "paths": { + "p0": { + "constraints": constraints_path, + "object": "on", + "predicates": None, + "subject": "sn" + } + } + } + return q + + +def generate_score_results(results, method='infores'): + """ + Generates a score dict, and a list of "analyses". + method can be 'infores' or 'edges' + """ + graph_scores = {} + max_score = 0 + auxiliary_graphs = results['auxiliary_graphs'] + for k, graph in auxiliary_graphs.items(): + if method == 'infores': + sources = set() + for edge_index in graph: + edge = results['knowledge_graph']['edges'][edge_index] + for resource in edge['sources']: + sources.add(resource['resource_id']) + score = len(sources) + if score > max_score: + max_score = score + else: + score = len(graph) + if score > max_score: + max_score = score + graph_scores[k] = score + graph_scores_formatted = [] + for k in graph_scores.keys(): + graph_scores[k] = graph_scores[k]/max_score + graph_scores_formatted.append({ + 'attributes': None, + 'path_bindings': { + 'p0': [{'id': k}]}, + 'resource_id': 'infores:tct', + 'score': graph_scores[k], + 'scoring_method': None, + 'support_graphs': None + }) + return graph_scores, graph_scores_formatted + + +def parse_results_for_pathfinder(start_node_id:str, end_node_id:str, result1:dict, result2:dict, + start_node_categories=None, end_node_categories=None, + get_node_info=True, + scoring_method='infores'): + """ + Converts the results of two TRAPI queries into the same general json format as the other pathfinder APIs. + scoring_method is how the node scores are generated, and could be 'infores' or 'edges'. + """ + # nodes + # TODO: get some node info? node attributes + node_info = {} + # edges is a dict of intermediate nodes + intermediate_node_edges = {} + for k, v in result1.items(): + i1 = v['subject'] + i2 = v['object'] + s_o = 'object' + if i1 == start_node_id: + intermediate_node_id = i2 + s_o = 'object' + elif i2 == start_node_id: + intermediate_node_id = i1 + s_o = 'subject' + else: + continue + if (i1 == start_node_id or i2 == start_node_id) and intermediate_node_id in intermediate_node_edges: + intermediate_node_edges[intermediate_node_id].append((k, v)) + else: + intermediate_node_edges[intermediate_node_id] = [(k, v)] + # add node dict + if intermediate_node_id not in node_info: + node_dict = { + } + node_info[intermediate_node_id] = node_dict + else: + node_dict = node_info[intermediate_node_id] + for attribute in v['attributes']: + if attribute['attribute_type_id'] == f'{s_o}_category': + if 'categories' not in node_dict: + node_dict['categories'] = set([attribute['value']]) + else: + node_dict['categories'].add(attribute['value']) + if attribute['attribute_type_id'] == f'{s_o}_name' and 'name' not in node_dict: + node_dict['name'] = attribute['value'] + node_info[intermediate_node_id] = node_dict + connecting_intermediate_nodes = {} + for k, v in result2.items(): + i1 = v['subject'] + i2 = v['object'] + if i1 == end_node_id: + intermediate_node_id = i2 + s_o = 'object' + elif i2 == end_node_id: + intermediate_node_id = i1 + s_o = 'subject' + else: + continue + if (i1 == end_node_id or i2 == end_node_id) and intermediate_node_id in intermediate_node_edges: + if intermediate_node_id in connecting_intermediate_nodes: + connecting_intermediate_nodes[intermediate_node_id]['e2'].append((k, v)) + else: + connecting_intermediate_nodes[intermediate_node_id] = {'e1': intermediate_node_edges[intermediate_node_id], 'e2' : [(k, v)]} + if intermediate_node_id not in node_info: + node_dict = { + } + node_info[intermediate_node_id] = node_dict + else: + node_dict = node_info[intermediate_node_id] + for attribute in v['attributes']: + if attribute['attribute_type_id'] == f'{s_o}_category': + if 'categories' not in node_dict: + node_dict['categories'] = set([attribute['value']]) + else: + node_dict['categories'].add(attribute['value']) + if attribute['attribute_type_id'] == f'{s_o}_name' and 'name' not in node_dict: + node_dict['name'] = attribute['value'] + node_info[intermediate_node_id] = node_dict + for k, v in node_info.items(): + if 'categories' in v: + v['categories'] = list(v['categories']) + all_edges = {} + all_auxiliary_graphs = {} + i = 1 + # sort connecting_intermediate_nodes by total number of connections + connection_counts = Counter({k: len(v['e1'])*len(v['e2']) for k, v in connecting_intermediate_nodes.items()}) + for i1, count in connection_counts.most_common(): + kv = connecting_intermediate_nodes[i1] + e1s = kv['e1'] + e2s = kv['e2'] + edges = {k: v for k, v in e1s} + edges.update({k: v for k, v in e2s}) + all_edges.update(edges) + keys = [x[0] for x in e1s] + [x[0] for x in e2s] + all_auxiliary_graphs[f'aux_{i}_{i1}'] = keys + i += 1 + # generate output json + output = { + 'query_graph': build_query_graph(start_node_id, end_node_id, start_node_categories, end_node_categories), + # TODO: don't drop the nodes + 'knowledge_graph': {'nodes': {x: node_info[x] for x in connection_counts.keys()}, + 'edges': all_edges, + }, + 'results': [{'analyses': []}], + 'auxiliary_graphs': all_auxiliary_graphs + } + graph_scores, graph_scores_formatted = generate_score_results(output, method=scoring_method) + output['results'][0]['analyses'] = graph_scores_formatted + if get_node_info: + from .node_normalizer import get_normalized_nodes + nodes_to_add = [] + for k, v in output['knowledge_graph']['nodes'].items(): + if 'name' not in v or 'categories' not in v: + nodes_to_add.append(k) + if nodes_to_add: + normalized_nodes = get_normalized_nodes(nodes_to_add, mode='post') + for node_id in nodes_to_add: + nn = normalized_nodes.get(node_id) + if nn is not None: + output['knowledge_graph']['nodes'][node_id] = {'name': nn.label, 'categories': nn.types} + return output + + +def pathfinder(input_node1_id:str, input_node2_id:str, + intermediate_categories:list, APInames, metaKG, API_predicates, + scoring_method='infores'): + """ + Returns a Pathfinder output for the given pair of nodes. scoring_method could be 'infores' or 'edges'. + """ + # get categories for input nodes + normalized_node_dict = node_normalizer.get_normalized_nodes([input_node1_id, input_node2_id]) + input_node1_info = normalized_node_dict[input_node1_id] + input_node1_list = [input_node1_id] + input_node1_category = input_node1_info.types + + input_node2_info = normalized_node_dict[input_node2_id] + print(input_node2_id) + input_node2_list = [input_node2_id] + + input_node2_category = input_node2_info.types + + # Select predicates and APIs based on the intermediate categories + sele_predicates1, sele_APIs1, API_URLs1 = sele_predicates_API(input_node1_category, + intermediate_categories, + metaKG, APInames) + sele_predicates2, sele_APIs2, API_URLs2 = sele_predicates_API(intermediate_categories, + input_node2_category, + metaKG, APInames) + query_json1 = translator_query.format_query_json(input_node1_list, # a list of identifiers for input node1 + [], # id list for the intermediate node, it can be empty list if only want to query node1 + input_node1_category, # a list of categories of input node1 + intermediate_categories, # a list of categories of the intermediate node + sele_predicates1) # a list of predicates + + # for the second hop, we want the predicates to be... + query_json2 = translator_query.format_query_json([], + input_node2_list, + intermediate_categories, # a list of categories of input node2 + input_node2_category, # a list of categories of the intermediate node + sele_predicates2) # a list of predicates + + result1 = translator_query.parallel_api_query(query_json=query_json1, + select_APIs = sele_APIs1, + APInames=APInames, + API_predicates=API_predicates, + max_workers=len(sele_APIs1)) + result2 = translator_query.parallel_api_query(query_json=query_json2, + select_APIs = sele_APIs2, + APInames=APInames, + API_predicates=API_predicates, + max_workers=len(sele_APIs2)) + output = parse_results_for_pathfinder(input_node1_id, input_node2_id, result1, result2, + start_node_categories=input_node1_category, + end_node_categories=input_node2_category, + scoring_method=scoring_method, + get_node_info=True) + + return result1, result2, output + + + +# define a function that uses the query_json as an template and change the ids and categories of the nodes +def format_pathfinder_query(node1_id, node1_category, node2_id, node2_category): + ''' + + ''' + query_json = { + "message": { + "query_graph": { + "nodes": { + "SN": { + "ids": [ + node1_id + ], + "categories": [ + node1_category + ] + }, + "ON": { + "ids": [ + node2_id + ], + "categories": [ + node2_category + ] + } + }, + "paths": { + "qpath": { + "subject": "SN", + "object": "ON" + } + } + } + }, + "submitter": "TCT", + } + return query_json + + +def query_aragorn_pathfinder(node1_id, node1_category, node2_id, node2_category): + aragorn_endpoint = 'https://shepherd.renci.org/aragorn/query' + #aragorn_endpoint = 'https://shepherd.ci.transltr.io/aragorn/query' new ci endpoint + query_current = format_pathfinder_query(node1_id, node1_category, node2_id, node2_category) + response = requests.post(aragorn_endpoint, json=query_current) + return response + + +def query_aragorn_pathfinder_with_constraints(node1_id, node1_category, node2_id, node2_category, constraints): + aragorn_endpoint = 'https://shepherd.renci.org/aragorn/query' + #aragorn_endpoint = 'https://shepherd.ci.transltr.io/aragorn/query' # new ci endpoint + query_current = format_query_json_for_pathfinder_with_constraints( + subject_ids=node1_id, + object_ids=node2_id, + subject_categories=node1_category, + object_categories=node2_category, + constraints=constraints + ) + response = requests.post(aragorn_endpoint, json=query_current) + return response + +def query_arax_pathfinder(node1_id, node1_category, node2_id, node2_category): + ARAX_endpoint = 'https://arax.ci.transltr.io/api/arax/v1.4/query' + query_current = format_pathfinder_query(node1_id, node1_category, node2_id, node2_category) + response = requests.post(ARAX_endpoint, json=query_current) + return response + +def query_arax_pathfinder_with_constraints(node1_id, node1_category, node2_id, node2_category, constraints): + ARAX_endpoint = 'https://arax.ci.transltr.io/api/arax/v1.4/query' + query_current = format_query_json_for_pathfinder_with_constraints( + subject_ids=node1_id, + object_ids=node2_id, + subject_categories=node1_category, + object_categories=node2_category, + constraints=constraints + ) + response = requests.post(ARAX_endpoint, json=query_current) + return response diff --git a/TCT/__init__.py b/TCT/__init__.py index 350d07e..e0e714f 100644 --- a/TCT/__init__.py +++ b/TCT/__init__.py @@ -3,4 +3,4 @@ from .translator_node import TranslatorNode as TranslatorNode -from . import name_resolver as name_resolver, node_normalizer as node_normalizer, trapi as trapi, translator_kpinfo as translator_kpinfo +from . import name_resolver as name_resolver, node_normalizer as node_normalizer, node_annotator as node_annotator, trapi as trapi, translator_kpinfo as translator_kpinfo diff --git a/TCT/graph_downloader.py b/TCT/graph_downloader.py new file mode 100644 index 0000000..85db2d1 --- /dev/null +++ b/TCT/graph_downloader.py @@ -0,0 +1,67 @@ +# Download graphs to local caches... + +import os +from pathlib import Path +import tarfile +import tempfile + +import requests +from zstandard import ZstdDecompressor + +GRAPHS = { + 'signor': { + 'download': 'https://kgx-storage.rtx.ai/releases/signor/latest/signor.tar.zst', + 'metadata': 'https://kgx-storage.rtx.ai/releases/signor/latest/graph-metadata.json' + }, +} + +CACHE_DIR = Path.home() / '.cache' / 'TCT' + +os.makedirs(CACHE_DIR, exist_ok=True) + +def download_graph(graph_name: str): + graph_path = CACHE_DIR / graph_name + download_path = GRAPHS[graph_name]['download'] + save_path = graph_path / (graph_name + '.tar.zst') + request = requests.get(download_path, stream=True) + with open(save_path, 'wb') as f: + for chunk in request.iter_content(chunk_size=16*1024): + f.write(chunk) + # Extract file with zstandard + dctx = ZstdDecompressor() + # source: https://gist.github.com/scivision/ad241e9cf0474e267240e196d7545eca + with tempfile.TemporaryFile(suffix=".tar") as ofh: + with save_path.open("rb") as ifh: + dctx.copy_stream(ifh, ofh) + ofh.seek(0) + with tarfile.open(fileobj=ofh) as z: + z.extractall(graph_path) + +def load_graph(graph_name: str, output='igraph'): + """ + Loads a Translator graph into igraph. + + Params + ------ + graph_name : str + The name of the graph - it should be in graph_downloader.GRAPHS. + """ + if graph_name not in GRAPHS.keys(): + raise ValueError('graph_name not found') + graph_path = CACHE_DIR / graph_name + metadata_path = graph_path / 'graph-metadata.json' + nodes_path = graph_path / 'nodes.jsonl' + edges_path = graph_path / 'edges.jsonl' + os.makedirs(graph_path, exist_ok=True) + # download metadata and main download + if not os.path.exists(metadata_path) or not os.path.exists(nodes_path) or not os.path.exists(edges_path): + # Download the .tar.zst file + download_graph(graph_name) + # load graph + from . import kg_loader + nodes, edges, node_types, edge_types = kg_loader.import_kg2_jsonl(nodes_path, edges_path) + if output == 'igraph': + return kg_loader.load_kg2_igraph_from_data(nodes, edges, node_types, edge_types) + #else: + # TODO: not implemented yet + # return kg_loader.load_kg2_networkx_from_data(nodes, edges, node_types, edge_types) diff --git a/TCT/kg_loader.py b/TCT/kg_loader.py new file mode 100644 index 0000000..aca9909 --- /dev/null +++ b/TCT/kg_loader.py @@ -0,0 +1,379 @@ +# Import graph from kg2 csv dump, tsv dump, or jsonl dump. + +import csv +import gzip +import json +import os + +from scipy import sparse, io + +# TODO: multiple edges between two nodes? +def import_kg2_csv(node_filename, edge_filename, edges_to_include=None, remove_unused_nodes=False, verbose=True, reindex_edges=True, use_node_types=True, use_edge_types=True, use_edge_properties=False): + """ + Args: + csv_filename: name of csv file (could be csv or tsv, or csv.gz or tsv.gz) + edges_to_include: set of edge types + remove_unused_nodes: True if nodes with no in- or out-edges are to be removed. + reindex_edges: whether or not to use indices or original IDs in the edge list. + + Returns: + nodes: list of (_id, _name, _labels_id) where _labels_id corresponds to a key in node_types + edges: dict of (node1, node2): _type_id where node1 and node2 index into nodes, and _type_id corresponds to a key in edge_types + node_types: dict of int: str (_labels) + edge_types: dict of int: str (_type) + """ + nodes = [] + n_nodes = 0 + # mapping of _id to index in nodes + node_index = {} + # node_types is a map of string ( + node_types = {} + edges = {} + # edge_types is a map of string (_type) to node + edge_types = {} + # sets of nodes that have in-edges or out-edges (to use when deciding whether to remove nodes) + node_has_edge = set() + csv.field_size_limit(99999999) + if node_filename.endswith('.gz'): + # handle gzip + f = gzip.open(node_filename, 'rt') + else: + f = open(node_filename) + delimiter = ',' + if 'tsv' in node_filename: + delimiter = '\t' + dr = csv.DictReader(f, dialect='unix', delimiter=delimiter) + for i, row in enumerate(dr): + if verbose and i % 100000 == 0: + print(i, 'nodes: ', len(node_index), 'edges: ', len(edges)) + # if this is a node + row_name = '' + row_identifier = row['id'] + if 'name' not in row: + row_name = row_identifier + else: + row_name = row['name'] + row_source = row['category'] + row_label = row['category'] + if use_node_types: + if row_label in node_types: + nodes.append((row['id'], row_name, node_types[row_label], row_identifier, row_source)) + else: + nodes.append((row['id'], row_name, len(node_types) + 1, row_identifier, row_source)) + node_types[row_label] = len(node_types) + 1 + else: + nodes.append((row['id'], row_name, True, row_identifier, row_source)) + node_index[row['id']] = n_nodes + n_nodes += 1 + if edge_filename.endswith('.gz'): + # handle gzip + f = gzip.open(edge_filename, 'rt') + else: + f = open(edge_filename) + delimiter = ',' + if 'tsv' in edge_filename: + delimiter = '\t' + dr = csv.DictReader(f, dialect='unix', delimiter=delimiter) + for i, row in enumerate(dr): + # if this row is an edge + edge_type = row['predicate'] + if edges_to_include is None or edge_type in edges_to_include: + node1 = row['subject'] + node2 = row['object'] + node_has_edge.add(node1) + node_has_edge.add(node2) + if use_edge_types: + if edge_type in edge_types: + edges[(node1, node2)] = edge_types[edge_type] + else: + edges[(node1, node2)] = len(edge_types) + 1 + edge_types[edge_type] = len(edge_types) + 1 + else: + edges[(node1, node2)] = True + if use_edge_properties: + if 'properties' in row: + edge_properties = row['properties'] + else: + edge_properties = {} + if 'primary_knowledge_source' in row: + edge_properties['primary_knowledge_source'] = row['primary_knowledge_source'] + edge_properties['id'] = int(row['id']) + edges[(node1, node2)] = edge_properties + if remove_unused_nodes: + # remove all nodes that don't have edges + to_remove = set(node_index.keys()).difference(node_has_edge) + nodes = [n for n in nodes if n[0] not in to_remove] + # rebuild node_index + node_index = {n[0]: i for i, n in enumerate(nodes)} + # convert edge indices + if reindex_edges: + new_edges = {} + for k, e in edges.items(): + node1, node2 = k + node1 = node_index[node1] + node2 = node_index[node2] + new_edges[(node1, node2)] = e + edges = new_edges + node_types = {v: k for k, v in node_types.items()} + edge_types = {v: k for k, v in edge_types.items()} + return nodes, edges, node_types, edge_types + + +def import_kg2_jsonl(node_filename, edge_filename, edges_to_include=None, remove_unused_nodes=True, use_edge_types=True, use_node_types=True, verbose=True, reindex_edges=True, use_edge_properties=False): + """ + Imports a jsonl file that contains nodes and edges. + + Args: + node_filename: name of jsonl file containing nodes, or both nodes and edges. + edge_filename: name of jsonl file containing edges. Can be None if all nodes and edges are in node_filename. + edges_to_include: set of edge types + remove_unused_nodes: True if nodes with no in- or out-edges are to be removed. + reindex_edges: whether or not to use indices or original IDs in the edge list. + + Returns: + nodes: list of (_id, _name, _labels_id) where _labels_id corresponds to a key in node_types + edges: dict of (node1, node2): _type_id where node1 and node2 index into nodes, and _type_id corresponds to a key in edge_types + node_types: dict of int: str (_labels) + edge_types: dict of int: str (_type) + """ + nodes = [] + n_nodes = 0 + # mapping of _id to index in nodes + node_index = {} + # node_types is a map of string ( + node_types = {} + edges = {} + # edge_types is a map of string (_type) to node + edge_types = {} + # sets of nodes that have in-edges or out-edges (to use when deciding whether to remove nodes) + node_has_edge = set() + if str(node_filename).endswith('.gz'): + # handle gzip + f = gzip.open(node_filename, 'rt') + else: + f = open(node_filename) + line = f.readline() + i = 0 + using_edge_file = False + while line: + row = json.loads(line) + if verbose and i % 100000 == 0: + print(i, 'nodes: ', len(node_index), 'edges: ', len(edges)) + print(row) + # if this is a node + if 'id' in row and 'category' in row and 'subject' not in row and 'object' not in row: + row_name = '' + row_identifier = row['id'] + if 'name' not in row: + row_name = row_identifier + else: + row_name = row['name'] + row_source = row['category'] + # arbitrarily pick the first category... + row_label = row['category'][0] + #print('row_label:', row_label) + if use_node_types: + if row_label in node_types: + nodes.append((row['id'], row_name, node_types[row_label], row_identifier, row_source)) + else: + nodes.append((row['id'], row_name, len(node_types) + 1, row_identifier, row_source)) + node_types[row_label] = len(node_types) + 1 + else: + nodes.append((row['id'], row_name, True, row_identifier, row_source)) + node_index[row['id']] = n_nodes + n_nodes += 1 + # if this row is an edge + else: + edge_type = row['predicate'] + if edges_to_include is None or edge_type in edges_to_include: + node1 = row['subject'] + node2 = row['object'] + node_has_edge.add(node1) + node_has_edge.add(node2) + if use_edge_types: + if edge_type in edge_types: + edges[(node1, node2)] = edge_types[edge_type] + else: + edges[(node1, node2)] = len(edge_types) + 1 + edge_types[edge_type] = len(edge_types) + 1 + else: + edges[(node1, node2)] = True + if use_edge_properties: + if 'properties' in row: + edge_properties = row['properties'] + else: + edge_properties = {} + if 'primary_knowledge_source' in row: + edge_properties['primary_knowledge_source'] = row['primary_knowledge_source'] + edge_properties['id'] = int(row['id']) + edges[(node1, node2)] = edge_properties + line = f.readline() + if not line and not using_edge_file and edge_filename is not None: + f.close() + print('Opening edge file', edge_filename) + if str(edge_filename).endswith('.gz'): + # handle gzip + f = gzip.open(edge_filename, 'rt') + else: + f = open(edge_filename) + line = f.readline() + using_edge_file = True + i += 1 + if remove_unused_nodes: + # remove all nodes that don't have edges + to_remove = set(node_index.keys()).difference(node_has_edge) + nodes = [n for n in nodes if n[0] not in to_remove] + # rebuild node_index + node_index = {n[0]: i for i, n in enumerate(nodes)} + # convert edge indices + if reindex_edges: + new_edges = {} + for k, e in edges.items(): + node1, node2 = k + node1 = node_index[node1] + node2 = node_index[node2] + new_edges[(node1, node2)] = e + edges = new_edges + node_types = {v: k for k, v in node_types.items()} + edge_types = {v: k for k, v in edge_types.items()} + return nodes, edges, node_types, edge_types + + + + +def to_sparse(nodes, edges): + """ + Returns a DOK matrix from the edges... + """ + n_nodes = len(nodes) + edge_matrix = sparse.dok_array((n_nodes, n_nodes), dtype=int) + for k, v in sorted(edges.items()): + n1, n2 = k + edge_matrix[n1, n2] = v + return edge_matrix + + +def load_kg2(filename='kg2.csv', edges_to_include=None, remove_unused_nodes=False, mtx_filename='spoke.mtx', **kwargs): + if filename.endswith('.csv') or filename.endswith('.csv.gz') or filename.endswith('.tsv') or filename.endswith('.tsv.gz'): + nodes, edges, node_types, edge_types = import_kg2_csv(filename, edges_to_include, remove_unused_nodes, **kwargs) + elif filename.endswith('.json') or filename.endswith('.json.gz') or filename.endswith('.jsonl') or filename.endswith('.jsonl.gz'): + nodes, edges, node_types, edge_types = import_kg2_jsonl(filename, edges_to_include, remove_unused_nodes, **kwargs) + else: + raise Exception('Filename should be a csv, tsv, json, or jsonl.') + if not os.path.exists(mtx_filename): + edge_matrix = to_sparse(nodes, edges) + io.mmwrite(mtx_filename, edge_matrix) + else: + edge_matrix = io.mmread(mtx_filename) + return nodes, edges, node_types, edge_types, edge_matrix + + +def load_kg2_igraph_from_data(nodes, edges, node_types, edge_types, remove_unused_nodes=True, directed=False, verbose=False, low_memory=False, **kwargs): + """ + Uses the output of import_kg2_csv or import_kg2_jsonl + """ + import igraph as ig + if low_memory: + edge_list = ({'s': str(v[0]), 't': str(v[1])} for v in edges.keys()) + del edges + else: + if 'use_edge_properties' in kwargs and kwargs['use_edge_properties'] == True: + # igraph doesn't allow lists as edge properties, so we are converting them to a string. + for v, e in edges.items(): + for key, value in e.copy().items(): + if isinstance(value, list) or isinstance(value, dict): + e[key] = str(value) + edge_list = ({'source': str(v[0]), 'target': str(v[1]), **e} for v, e in edges.items()) + else: + edge_list = ({'source': str(v[0]), 'target': str(v[1]), 'type': edge_types[e]} for v, e in edges.items()) + if verbose: + print('creating node list') + # set node attributes + # convert the node id to a string, bc + if low_memory: + node_list = ({ + 'name': str(n[0]), + 'feature_name': n[1], + 'category': node_types[n[2]], + 'identifier': n[3], + } for n in nodes) + del nodes + else: + node_list = ({ + 'name': str(n[0]), + 'feature_name': n[1], + 'category': node_types[n[2]], + 'identifier': n[3], + 'source': n[4], + } for n in nodes) + if verbose: + print('calling igraph.Graph.DictList') + if low_memory: + graph = ig.Graph.DictList(node_list, edge_list, directed=directed, + edge_foreign_keys=('s', 't'), + iterative=False) + else: + graph = ig.Graph.DictList(node_list, edge_list, directed=directed, + edge_foreign_keys=('source', 'target'), + iterative=False) + return graph + + +def load_kg2_networkx(filename='spoke.csv', edges_to_include=None, remove_unused_nodes=True, directed=False, **kwargs): + import networkx as nx + if filename.endswith('.csv') or filename.endswith('.csv.gz') or filename.endswith('.tsv') or filename.endswith('.tsv.gz'): + nodes, edges, node_types, edge_types = import_kg2_csv(filename, edges_to_include, remove_unused_nodes, reindex_edges=False, **kwargs) + elif filename.endswith('.json') or filename.endswith('.json.gz') or filename.endswith('.jsonl') or filename.endswith('.jsonl.gz'): + nodes, edges, node_types, edge_types = import_kg2_jsonl(filename, edges_to_include, remove_unused_nodes, reindex_edges=False, **kwargs) + else: + raise Exception('Filename should be a csv, tsv, json, or jsonl.') + edge_list = edges.keys() + if directed: + graph = nx.from_edgelist(edge_list, nx.DiGraph) + else: + graph = nx.from_edgelist(edge_list) + # set node attributes + node_attributes = {} + # TODO: get all IDs, not just names + for n in nodes: + node_attributes[n[0]] = { + 'name': n[1], + 'category': node_types[n[2]], + 'identifier': n[3], + 'source': n[4], + } + nx.set_node_attributes(graph, node_attributes) + # set edge attributes + edge_attributes = {k: {'type': edge_types[v]} for k, v in edges.items()} + nx.set_edge_attributes(graph, edge_attributes) + return graph + + +def load_kg2_igraph(filename='graph.jsonl.gz', edges_to_include=None, remove_unused_nodes=True, directed=False, verbose=False, low_memory=False, **kwargs): + """ + Imports the file as an igraph. The file can be a json/jsonl export from neo4j, and it can be gzipped. The spoke IDs are converted to strings because igraph is very slow if the ids are ints. + """ + if low_memory: + kwargs['use_edge_properties'] = False + if filename.endswith('.csv') or filename.endswith('.csv.gz') or filename.endswith('.tsv') or filename.endswith('.tsv.gz'): + nodes, edges, node_types, edge_types = import_kg2_csv(filename, edges_to_include, remove_unused_nodes, reindex_edges=False, **kwargs) + elif filename.endswith('.json') or filename.endswith('.json.gz') or filename.endswith('.jsonl') or filename.endswith('.jsonl.gz'): + nodes, edges, node_types, edge_types = import_kg2_jsonl(filename, edges_to_include, remove_unused_nodes, reindex_edges=False, **kwargs) + else: + raise Exception('Filename should be a csv, tsv, json, or jsonl.') + if verbose: + print('Done loading data, creating edge list') + return load_kg2_igraph_from_data(nodes, edges, node_types, edge_types, remove_unused_nodes, directed, verbose, low_memory, **kwargs) + # use igraph.graph.DictList + + +def symmetrize_matrix(matrix): + """ + Symmetrizes an adjacency matrix. + + Warning: this completely destroys any meaning applied to node values. Nonzero = edge exists, zero = edge doesn't exist. + """ + lower_triangle = sparse.tril(matrix) + upper_triangle = sparse.triu(matrix) + return lower_triangle + lower_triangle.T + upper_triangle + upper_triangle.T + diff --git a/TCT/name_resolver.py b/TCT/name_resolver.py index f0166c5..cf78e6a 100644 --- a/TCT/name_resolver.py +++ b/TCT/name_resolver.py @@ -12,10 +12,18 @@ URL = 'https://name-lookup.ci.transltr.io/' """This is the root URL for the API.""" +def status(): + """ + Returns the status of the Name Resolver API. + """ + response = requests.get(URL + 'status') + response.raise_for_status() + return response.json() -def lookup(query: str, return_top_response:bool=True, return_synonyms:bool=False, **kwargs): + +def lookup(query: str, return_top_response:bool=True, return_synonyms:bool=False, limit:int=10, **kwargs): """ - A wrapper around the `lookup` api endpoint. Given a query string, this returns a TranslatorNode object or a list of TranslatorNode objects corresponding to the given name. + A wrapper around the `lookup` api endpoint. Given a query string, this returns a TranslatorNode object or a list of TranslatorNode objects corresponding to the given name. Parameters ---------- @@ -25,6 +33,8 @@ def lookup(query: str, return_top_response:bool=True, return_synonyms:bool=False If true, this returns only the top response. If false, this returns a list of all responses. Default: True return_synonyms : bool If true, the resulting TranslatorNode objects contain a list of synonyms. If false, they do not include synonyms. Default: False + limit : int + The number of results to return. **kwargs Other arguments to `lookup`. Some possible arguments: `limit=20` would limit the results to 20. `autocomplete=True` indicates that the query string can be incomplete. `biolink_type="biolink:Disease"` indicates that all returned results should be diseases. `only_taxa='NCBITaxon:9606` indicates that only Homo sapiens results should be returned. For more examples, see [this](https://name-lookup.ci.transltr.io/docs#/lookup/lookup_curies_get_lookup_get). @@ -44,24 +54,14 @@ def lookup(query: str, return_top_response:bool=True, return_synonyms:bool=False # set autocomplete to be false by default if 'autocomplete' not in kwargs: kwargs['autocomplete'] = False - response = requests.get(path, params={'string': query, **kwargs}) + response = requests.get(path, params={'string': query, 'limit': limit, **kwargs}) if response.status_code == 200: result = response.json() if len(result) == 0: raise LookupError('No matching CURIE found for the given string ' + query) else: if return_top_response: - node = result[0] - n = TranslatorNode(node['curie']) - if 'label' in node: - n.label = node['label'] - if 'types' in node: - n.types = node['types'] - if 'taxa' in node: - n.taxa = node['taxa'] - if return_synonyms and 'synonyms' in node: - n.synonyms = node['synonyms'] - return n + return TranslatorNode.from_dict(result[0], return_synonyms) else: all_nodes = [] for node in result: @@ -72,14 +72,14 @@ def lookup(query: str, return_top_response:bool=True, return_synonyms:bool=False raise requests.RequestException('Response from server had error, code ' + str(response.status_code) + ' ' + str(response)) -def synonyms(query: str, **kwargs): +def synonyms(query: str|list, **kwargs): """ - A wrapper around the `synonyms` api endpoint. Given a query string, this returns a dict of CURIE id : TranslatorNode for all synonyms for the given query. + A wrapper around the `synonyms` api endpoint. Given a CURIE or a list of CURIEs, this returns a dict of CURIE id : TranslatorNode for all synonyms for the given query. Parameters ---------- - query : str - Query CURIE + query : str|list + Query CURIE or list of CURIEs **kwargs Other arguments to `synonyms` @@ -93,12 +93,15 @@ def synonyms(query: str, **kwargs): if response.status_code == 200: result = response.json() if len(result) == 0: - raise LookupError('No matching CURIE found for the given string ' + query) + raise LookupError('No matching CURIE found for the given string ' + str(query)) else: all_nodes = {} for k, node in result.items(): - n = TranslatorNode.from_dict(node, return_synonyms) - all_nodes[k] = n + if not node: + # If node is empty or None. + all_nodes[k] = None + else: + all_nodes[k] = TranslatorNode.from_dict(node, return_synonyms=True) return all_nodes else: raise requests.RequestException('Response from server had error, code ' + str(response.status_code) + ' ' + str(response)) @@ -114,7 +117,7 @@ def chunk_list(data:list, size:int): def batch_lookup(strings:list[str], size: int=25, return_top_response:bool=True, return_synonyms:bool=False, **kwargs) -> dict: """ - A wrapper around the `bulk-lookup` api endpoint. Given a list of query strings, this returns a TranslatorNode object or a list of TranslatorNode objects corresponding to the given name. + A wrapper around the `bulk-lookup` api endpoint. Given a list of query strings, this returns a TranslatorNode object or a list of TranslatorNode objects corresponding to the given name. Parameters ---------- @@ -156,7 +159,7 @@ def batch_lookup(strings:list[str], size: int=25, return_top_response:bool=True, for s in chunk: nodes = result.get(s, []) translator_nodes = [] - for node in nodes: + for node in nodes: n = TranslatorNode.from_dict(node, return_synonyms) translator_nodes.append(n) if return_top_response: @@ -169,3 +172,42 @@ def batch_lookup(strings:list[str], size: int=25, return_top_response:bool=True, else: raise requests.RequestException('Response from server had error, code ' + str(response.status_code) + ' ' + str(response)) return curies + + +def batch_synonyms(strings:list[str], size:int=50, **kwargs) -> dict: + """ + A wrapper around the `synonyms` API endpoint, using POST. Given a list of CURIEs, this returns a dict of CURIE:TranslatorNode, where each TranslatorNode contains all synonyms for the given CURIE. + + Parameters + ---------- + strings : list[str] + List of CURIEs. + size : int + Desired chunking size, default is 50. + + Returns + ------- + Dict of CURIE : TranslatorNode + """ + chunks = chunk_list(strings, size) + path = urllib.parse.urljoin(URL, 'synonyms') + curies = {} + for chunk in chunks: + # set autocomplete to be false by default + response = requests.post(path, json={'preferred_curies': chunk, **kwargs}) + if response.status_code == 200: + result = response.json() + if len(result) == 0: + raise LookupError('No matching CURIE found for the given CURIEs ' + str(chunks)) + else: + for k, node in result.items(): + if not node: + # If node is empty or None. + curies[k] = None + else: + curies[k] = TranslatorNode.from_dict(node, return_synonyms=True) + else: + raise requests.RequestException('Response from server had error, code ' + str(response.status_code) + ' ' + str(response)) + return curies + + diff --git a/TCT/node_annotator.py b/TCT/node_annotator.py new file mode 100644 index 0000000..af85212 --- /dev/null +++ b/TCT/node_annotator.py @@ -0,0 +1,66 @@ +""" +This is a wrapper around the Node Annotator API. + +API docs: https://annotator.transltr.io/ +""" +import urllib.parse + +import requests + +from .translator_node import TranslatorNode + +URL = 'https://annotator.transltr.io/' +"""This is the root URL for the API.""" + +def status(): + """ + Returns the status of the Node Annotator API. + """ + response = requests.get(f'{URL}status') + response.raise_for_status() + return response.json() + + +def lookup_curie(curie: str, **kwargs): + return lookup_curies([curie], **kwargs)[curie] + + +def lookup_curies(curies: list[str], **kwargs): + """ + A wrapper around the `curies` API endpoint. Given a list of CURIEs, this returns a dictionary where each + CURIE is mapped to a list of annotations. + + Parameters + ---------- + curies : list[str] + A list of CURIEs to look up. + **kwargs + Other arguments to `curie`. Some possible arguments: `raw=true` returns annotation fields in their original + data structure before transformation, `fields` can be used to provide a comma-separated list of annotation fields + you are interested in, and `include_extra=true` (default true) uses external APIs to provide additional annotations. + + Returns + ------- + A dictionary with keys as the input CURIEs and the values as dictionaries of annotations and their values. + + Examples + -------- + >>> lookup_curies(['MESH:D014867']) + >>> lookup_curies(['NCIT:C34373', 'NCBIGene:1756']) + """ + path = urllib.parse.urljoin(URL, 'curie') + response = requests.post(path, json={'ids': curies, **kwargs}) + response.raise_for_status() + + result = response.json() + if len(result) == 0: + raise LookupError('No matching CURIE found for the given string ' + curies) + + results = response.json() + + for curie in results: + # NodeAnnotator sometimes return a list of a single item. If so, we can unwrap it here. + if len(results[curie]) == 1: + results[curie] = results[curie][0] + + return results diff --git a/TCT/node_normalizer.py b/TCT/node_normalizer.py index 4bae503..bb7e374 100644 --- a/TCT/node_normalizer.py +++ b/TCT/node_normalizer.py @@ -10,15 +10,23 @@ from .translator_node import TranslatorNode -URL = 'https://nodenorm.transltr.io/' +URL = 'https://nodenorm.ci.transltr.io/' + +def status(): + """ + Returns the status of the Node Normalizer API. + """ + response = requests.get(f'{URL}status') + response.raise_for_status() + return response.json() def get_normalized_nodes(query: str | list[str], return_equivalent_identifiers:bool=False, mode:str='get', - **kwargs): + **kwargs) -> dict[str, TranslatorNode]|TranslatorNode: """ A wrapper around the `get_normalized_nodes` api endpoint. Given a CURIE or a list of CURIEs, this returns either a single TranslatorNode or a dict of CURIE ids to TranslatorNodes. - + Parameters ---------- query : str @@ -41,42 +49,49 @@ def get_normalized_nodes(query: str | list[str], >>> get_normalized_nodes('MESH:D014867', return_equivalent_identifiers=False) TranslatorNode(curie='CHEBI:15377', label='Water', types=['biolink:SmallMolecule', 'biolink:MolecularEntity', 'biolink:ChemicalEntity', 'biolink:PhysicalEssence', 'biolink:ChemicalOrDrugOrTreatment', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:ChemicalEntityOrProteinOrPolypeptide', 'biolink:NamedThing', 'biolink:PhysicalEssenceOrOccurrent'], synonyms=None, curie_synonyms=None) """ - path = urllib.parse.urljoin(URL, 'get_normalized_nodes') # default parameters: true for gene-protein conflation, false for drug-chemical conflation + path = urllib.parse.urljoin(URL, 'get_normalized_nodes') + # TODO: batch the query? if mode == 'post': - response = requests.post(path, json={'curies': query, **kwargs}) + if isinstance(query, str): + # CURIEs sent to POST must be a list. If a single CURIE is given, we wrap it. + json_query = [query] + else: + json_query = query + if len(json_query) == 0: + return {} + response = requests.post(path, json={'curies': json_query, **kwargs}) else: response = requests.get(path, params={'curie': query, **kwargs}) if response.status_code == 200: result = response.json() - if len(result) == 0: - raise LookupError('No matches found for the given input: ' + str(query)) - else: - normalized_dict = {} - for k, node in result.items(): - if node is None: - normalized_dict[k] = None - continue - n = TranslatorNode(node['id']['identifier']) - if 'label' in node['id']: - n.label = node['id']['label'] - if 'type' in node: - n.types = node['type'] - if return_equivalent_identifiers and 'equivalent_identifiers' in node: - synonyms = [] - curie_synonyms = [] - for eq in node['equivalent_identifiers']: - if 'label' in eq: - synonyms.append(eq['label']) - else: - synonyms.append(None) - curie_synonyms.append(eq['identifier']) - n.synonyms = synonyms - n.curie_synonyms = curie_synonyms - normalized_dict[k] = n - if isinstance(query, str): - return normalized_dict[query] - return normalized_dict + normalized_dict = {} + for k, node in result.items(): + if node is None: + # No match found for CURIE `k`. + normalized_dict[k] = None + continue + + n = TranslatorNode(node['id']['identifier']) + if 'label' in node['id']: + n.label = node['id']['label'] + if 'type' in node: + n.types = node['type'] + if return_equivalent_identifiers and 'equivalent_identifiers' in node: + synonyms = [] + curie_synonyms = [] + for eq in node['equivalent_identifiers']: + if 'label' in eq: + synonyms.append(eq['label']) + else: + synonyms.append(None) + curie_synonyms.append(eq['identifier']) + n.synonyms = synonyms + n.curie_synonyms = curie_synonyms + normalized_dict[k] = n + if isinstance(query, str): + return normalized_dict[query] + return normalized_dict else: raise requests.RequestException('Response from server had error, code ' + str(response.status_code)) @@ -103,6 +118,8 @@ def get_preferred_names(id_list:list[str], batch_limit=500, **kwargs) -> dict[st for index in range(0, len(id_list), batch_limit): id_sublist = id_list[index:index + batch_limit] normalized_nodes = get_normalized_nodes(id_sublist, mode='post', **kwargs) + if isinstance(normalized_nodes, TranslatorNode): + normalized_nodes = {normalized_nodes.curie: normalized_nodes} for curie in id_sublist: if curie not in normalized_nodes or normalized_nodes[curie] is None: unmapped_ids.append(curie) diff --git a/TCT/translator_metakg.py b/TCT/translator_metakg.py index e8ec14b..b76b9d6 100644 --- a/TCT/translator_metakg.py +++ b/TCT/translator_metakg.py @@ -3,15 +3,21 @@ import pandas as pd -def find_link(name): +def find_link(name, use_new_url=True): #pre = "https://dev.smart-api.info/api/metakg/consolidated?size=2000&q=%28api.x-translator.component%3AKP+AND+api.name%3A" # This works for the previous version - pre = "https://smart-api.info/api/metakg/consolidated?size=2000&q=%28api.x-translator.component%3AKP+AND+api.name%3A" - end = "%5C%28Trapi+v1.5.0%5C%29%29" + if use_new_url: + pre = "https://smart-api.info/api/metakg?size=5000&q=(api.x-translator.component:KP+AND+api.name:" + end = ")&facet_size=300&aggs=object.raw,subject.raw" + else: + pre = "https://smart-api.info/api/metakg/consolidated?size=5000&q=%28api.x-translator.component%3AKP+AND+api.name%3A" + end = "%5C%28Trapi+v1.5.0%5C%29%29" + name = name.replace(' - ', ' ') if '(Trapi v1.5.0)' in name: url = pre name_raw = name.split("(")[0] words = name_raw.split(" ") + # TODO: replace '(Trapi v1.5.0)' with '\(Trapi+v1.5.0\)' length = len(words) if length == 1: url = url + words[0] + end @@ -19,7 +25,6 @@ def find_link(name): for i in range(0,length-1): url = url + words[i] + "+" url = url+words[length-1]+end - else: words = name.split(" ") url = pre @@ -27,11 +32,16 @@ def find_link(name): for i in range(0,length-1): url = url + words[i] + "+" - url = url+words[length-1]+"%29" - return(url) + url = url+words[length-1] + if use_new_url: + url += end + else: + url = url+"%29" + return url -def get_KP_metadata(APInames:dict[str, str]) -> pd.DataFrame: + +def get_KP_metadata(APInames:dict[str, str], use_new_url=True) -> pd.DataFrame: ''' This function is used to get the metadata of the KPs in the APInames dictionary. @@ -64,10 +74,17 @@ def get_KP_metadata(APInames:dict[str, str]) -> pd.DataFrame: if KP == "RTX KG2 - TRAPI 1.5.0": text =requests.get("https://smart-api.info/api/metakg/consolidated?size=20&q=%28api.x-translator.component%3AKP+AND+api.name%3ARTX+KG2+%5C-+TRAPI+1%5C.4%5C.0%29").text # This works for the previous version json_text = json.loads(text) - else: - text = requests.get(find_link(KP)).text + else: + text = requests.get(find_link(KP, use_new_url=use_new_url)).text json_text = json.loads(text) - + if 'hits' not in json_text: + if use_new_url: + #print(KP, '- no hits found in new metakg URL, trying old URL pattern') + text = requests.get(find_link(KP, use_new_url=False)).text + json_text = json.loads(text) + else: + print(KP, '- no hits found') + continue for i in (json_text['hits']): Predicate_list.append("biolink:"+i['_id'].split("-")[1]) API_list.append(KP) @@ -121,7 +138,7 @@ def add_new_API_for_query(APInames:dict[str, str], metaKG:pd.DataFrame, newAPIna return APInames, metaKG -def add_plover_API(APInames:dict[str, str], metaKG:pd.DataFrame): +def add_plover_API(APInames:dict[str, str], metaKG:pd.DataFrame) -> tuple[dict[str, str], pd.DataFrame]: ''' This function is used to add the Plover APIs developed by the CATRAX team to the APInames and metaKG. @@ -134,6 +151,8 @@ def add_plover_API(APInames:dict[str, str], metaKG:pd.DataFrame): Microbiome, and RTX KG2. + If an API endpoint is not available (i.e. returns a non-200 return code), then the API will not be included. + Parameters ---------- APInames : dict @@ -142,64 +161,116 @@ def add_plover_API(APInames:dict[str, str], metaKG:pd.DataFrame): metaKG : pandas.DataFrame This is the output of `get_kp_metadata`. - - - Examples -------- >>> APInames, metaKG = add_plover_API(APInames, metaKG) ''' import requests url = 'https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + try: + response = requests.get(url, timeout=5) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase. Status code:", response.status_code) + + + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase") + + url = 'https://multiomics.rtx.ai:9990/PharmacogenomicsKG/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "CATRAX Pharmacogenomics KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/PharmacogenomicsKG/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "CATRAX Pharmacogenomics KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/PharmacogenomicsKG/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/PharmacogenomicsKG. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/PharmacogenomicsKG") + + url = 'https://multiomics.rtx.ai:9990/ctkp/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Clinical Trials KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/ctkp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Clinical Trials KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/ctkp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/ctkp. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/ctkp") - url = 'https://multiomics.rtx.ai:9990/dakp/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Drug Approvals KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/dakp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + + url = 'https://multiomics.rtx.ai:9990/dakp/meta_knowledge_graph' + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Drug Approvals KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/dakp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/dakp. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/dakp") + + url = 'https://multiomics.rtx.ai:9990/mokp/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Multiomics KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/multiomics/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) - + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Multiomics KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/multiomics/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/mokp. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/mokp") + url = 'https://multiomics.rtx.ai:9990/mbkp/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Microbiome KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/mbkp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) - + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "Microbiome KP - TRAPI 1.5.0", "https://multiomics.rtx.ai:9990/mbkp/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/mbkp. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://multiomics.rtx.ai:9990/mbkp") + url = 'https://kg2cploverdb.ci.transltr.io/meta_knowledge_graph' - response = requests.get(url) - data = response.json() - for i in range(len(data["edges"])): - APInames, metaKG = add_new_API_for_query(APInames, metaKG, "RTX KG2 - TRAPI 1.5.0", "https://kg2cploverdb.ci.transltr.io/kg2c/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + try: + response = requests.get(url) + if response.status_code == 200: + data = response.json() + for i in range(len(data["edges"])): + APInames, metaKG = add_new_API_for_query(APInames, metaKG, "RTX KG2 - TRAPI 1.5.0", "https://kg2cploverdb.ci.transltr.io/kg2c/query", data["edges"][i]['predicate'], data["edges"][i]['subject'], data["edges"][i]['object']) + else: + print("Warning: Failed to retrieve data from the https://kg2cploverdb.ci.transltr.io. Status code:", response.status_code) + except requests.exceptions.RequestException: + print("Warning: Failed to retrieve data from the https://kg2cploverdb.ci.transltr.io") - return APInames, metaKG -def load_translator_resources(): +def load_translator_resources(use_new_metakg_url=False): """ Load the necessary resources for the Translator. + Params + ------ + use_new_metakg_url + If True, this uses https://smart-api.info/api/metakg. If False, this uses https://smart-api.info/api/metakg/consolidated? + Returns ------- APInames @@ -208,6 +279,9 @@ def load_translator_resources(): """ from .translator_kpinfo import get_translator_kp_info Translator_KP_info, APInames = get_translator_kp_info() - metaKG = get_KP_metadata(APInames) + metaKG = get_KP_metadata(APInames, use_new_url=use_new_metakg_url) + APInames, metaKG = add_plover_API(APInames, metaKG) + metaKG = metaKG[metaKG['Predicate'] != 'biolink:rdfs:subClassOf'] + return APInames, metaKG, Translator_KP_info diff --git a/TCT/translator_node.py b/TCT/translator_node.py index 79702c2..91ad148 100644 --- a/TCT/translator_node.py +++ b/TCT/translator_node.py @@ -80,11 +80,16 @@ def from_dict(cls, data_dict:dict, return_synonyms=False): if 'label' in data_dict: n.label = data_dict['label'] if 'types' in data_dict: - n.types = data_dict['types'] + # Do the types have the `biolink:` prefix? If not, add them. + n.types = list(map(lambda ty: f"biolink:{ty}" if not ty.startswith('biolink:') else ty, data_dict['types'])) if 'taxa' in data_dict: n.taxa = data_dict['taxa'] - if return_synonyms and 'synonyms' in node: - n.synonyms = node['synonyms'] + if return_synonyms: + if 'synonyms' in data_dict: + n.synonyms = data_dict['synonyms'] + elif 'names' in data_dict: + # NameRes refers to synonyms as "names". + n.synonyms = data_dict['names'] return n diff --git a/TCT/translator_query.py b/TCT/translator_query.py index 870b153..0fa57a9 100644 --- a/TCT/translator_query.py +++ b/TCT/translator_query.py @@ -43,6 +43,95 @@ def get_translator_API_predicates() -> tuple[dict, pandas.DataFrame, dict]: return APInames, metaKG, API_predicates + +def build_attribute_constraint(attribute_id, operator, value, name=None, is_not=False): + """ + This creates an attribute constraint for a TRAPI query dict. + + Example + ------- + """ + if name is None: + name = '' + output = { + 'id': attribute_id, + 'operator': operator, + 'value': value, + 'name': name + } + if is_not: + output['not'] = True + return output + + +def format_query_json(subject_ids:list[str], + object_ids:list[str]|None = None, + subject_categories:list[str]|None = None, + object_categories:list[str]|None = None, + predicates:list[str]|None = None, + attribute_constraints:list[dict]|None = None, + ) -> dict: + ''' + Formats a query dict, with optional constraints. + + Example input: + subject_ids = ["NCBIGene:3845"] + object_ids = [] + subject_categories = ["biolink:Gene"] + object_categories = ["biolink:Gene"] + predicates = ["biolink:positively_correlated_with", "biolink:physically_interacts_with"] + attribute_constraints = [build_attribute_constraint('biolink:has_total', '>', 2)] + ''' + #edited Dec 5, 2023 + query_json_temp = { + "message": { + "query_graph": { + + "edges": { + "e00": { + #"e1": { + "subject": "n00", + "object": "n01", + "predicates": predicates + } + }, + "nodes": { + "n00": { + "ids":subject_ids, # required + #"categories":[] # optional, if not provided, it will be empty + }, + "n01": { + #"ids":[], + "categories":[] # required + }} + } + }, + + "submitter": "TCT" + } + + if attribute_constraints is not None and len(attribute_constraints) > 0: + query_json_temp['message']['query_graph']['edges']['attribute_constraints'] = attribute_constraints + + if subject_ids is not None and len(subject_ids) > 0: + query_json_temp["message"]["query_graph"]["nodes"]["n00"]["ids"] = subject_ids + + if object_ids is not None and len(object_ids) > 0: + query_json_temp["message"]["query_graph"]["nodes"]["n01"]["ids"] = object_ids + + if subject_categories is not None and len(subject_categories) > 0: + query_json_temp["message"]["query_graph"]["nodes"]["n00"]["categories"] = subject_categories + + if object_categories is not None and len(object_categories) > 0: + query_json_temp["message"]["query_graph"]["nodes"]["n01"]["categories"] = object_categories + + if predicates is not None and len(predicates) > 0: + query_json_temp["message"]["query_graph"]["edges"]["e00"]["predicates"] = predicates + + return query_json_temp + + + def optimize_query_json(query_json, API_name_cur, API_predicates): ''' Optimize the query JSON by removing predicates that are not supported by the selected APIs. @@ -83,7 +172,7 @@ def query_KP(API_name_cur, query_json, APInames, API_predicates): Query an individual API with a TRAPI 1.5.0 query JSON, without modifying the original query_json. """ - API_url_cur = APInames[API_name_cur] + API_url_cur = APInames[API_name_cur].strip('/') # deep‐copy so we never touch the caller’s data query_copy = deepcopy(query_json) # optimize on our private copy diff --git a/TCT/trapi.py b/TCT/trapi.py index 9bad9b9..467988d 100644 --- a/TCT/trapi.py +++ b/TCT/trapi.py @@ -12,7 +12,7 @@ # TODO: incorporate object ids into the method. def build_query(subject_ids:list[str], object_categories:list[str], predicates:list[str], - return_json:bool=True, + return_json:bool=False, object_ids=None, subject_categories=None): """ This constructs a query json for use with TRAPI. Queries are of the form [subject_ids]-[predicates]-[object_categories]. @@ -33,7 +33,7 @@ def build_query(subject_ids:list[str], A list of predicates that we are interested in. Example: ["biolink:positively_correlated_with", "biolink:physically_interacts_with"]. return_json - If true, returns a json string; if false, returns a dict. + If true, returns a json string; if false, returns a dict (default). object_ids None by default @@ -42,7 +42,7 @@ def build_query(subject_ids:list[str], Returns ------- - A json string + A dict (default) or json string if return_json=True Examples -------- @@ -94,7 +94,7 @@ def process_result(result:dict): """ -def query(url:str, query:str): +def query(url:str, query:dict): """ Queries a single TRAPI endpoint. @@ -102,8 +102,8 @@ def query(url:str, query:str): ------ url : str The URL for the API endpoint. - query : str - A JSON string representing the query, as produced by build_query + query : dict + A dict representing the query, as produced by build_query Returns ------- @@ -111,11 +111,16 @@ def query(url:str, query:str): Examples -------- - >>> query = build_query(['NCBIGene:3845'], ['biolink:Gene'], ['biolink:physically_interacts_with']) - >>> response = query(url, query) + >>> query_dict = build_query(['NCBIGene:3845'], ['biolink:Gene'], ['biolink:physically_interacts_with']) + >>> response = query(url, query_dict) >>> print(response) """ - # example: 1. get APIs, 2. get APIs that have the target object and subject types, and the target predicates. 3. build the query and run the query. + if isinstance(query, str): + raise TypeError( + "query must be a dict, not a JSON string. " + "Use build_query(...) without return_json=True, " + "or pass json.loads(query) instead." + ) response = requests.post(url, json=query) if response.status_code == 200: # TODO diff --git a/coverage.xml b/coverage.xml deleted file mode 100644 index 9d2ba74..0000000 --- a/coverage.xml +++ /dev/null @@ -1,1811 +0,0 @@ - -| \n", - " | Pemigatinib | \n", - "Midostaurin | \n", - "Marqibo | \n", - "Gemtuzumab | \n", - "diarsenic trioxide | \n", - "Cytarabine | \n", - "DAUNOrubicin Hydrochloride Novaplus | \n", - "Rituximab | \n", - "Quizartinib | \n", - "GLASDEGIB MALEATE | \n", - "... | \n", - "Olutasidenib | \n", - "N-(Hydroxyethyl)doxorubicin hydrochloride | \n", - "Idarubicin | \n", - "Enasidenib | \n", - "Glasdegib | \n", - "Rituximab 10 MG/1 ML Intravenous Solution [RITUXAN] | \n", - "Azacitidine | \n", - "Ivosidenib | \n", - "Tioguanine | \n", - "Cyclophosphamide | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| biolink:treats | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "
1 rows × 33 columns
\n", - "| \n", - " | score | \n", - "output_node | \n", - "predictes1 | \n", - "predictes2 | \n", - "output_node_name | \n", - "
|---|---|---|---|---|---|
| Bortezomib | \n", - "0.642857 | \n", - "CHEBI:52717 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Bortezomib | \n", - "
| Tretinoin | \n", - "0.489796 | \n", - "CHEBI:15367 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Tretinoin | \n", - "
| Azacitidine | \n", - "0.428571 | \n", - "CHEBI:2038 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:inte... | \n", - "Azacitidine | \n", - "
| Sirolimus | \n", - "0.367347 | \n", - "CHEBI:9168 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:regulates; biolink:se... | \n", - "Sirolimus | \n", - "
| Decitabine | \n", - "0.367347 | \n", - "CHEBI:50131 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Decitabine | \n", - "
| Topotecan | \n", - "0.357143 | \n", - "CHEBI:63632 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Topotecan | \n", - "
| Cyclosporine | \n", - "0.326531 | \n", - "CHEBI:4031 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:regulates; biolink:af... | \n", - "Cyclosporine | \n", - "
| Venetoclax | \n", - "0.306122 | \n", - "CHEBI:133021 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:interacts_with; bioli... | \n", - "Venetoclax | \n", - "
| Vorinostat | \n", - "0.306122 | \n", - "CHEBI:45716 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Vorinostat | \n", - "
| Ascorbic acid | \n", - "0.275510 | \n", - "CHEBI:22652 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Ascorbic acid | \n", - "
| Daunorubicin hydrochloride | \n", - "0.275510 | \n", - "CHEBI:31456 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:interacts_with; biolink:affects; bioli... | \n", - "Daunorubicin hydrochloride | \n", - "
| Hydroxyurea | \n", - "0.244898 | \n", - "CHEBI:44423 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Hydroxyurea | \n", - "
| imatinib methanesulfonate | \n", - "0.244898 | \n", - "CHEBI:31690 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:occurs_together_in_literature_with; bi... | \n", - "imatinib methanesulfonate | \n", - "
| dexamethasone sodium phosphate | \n", - "0.244898 | \n", - "CHEBI:4462 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:occurs_together_in_literature_with; bi... | \n", - "dexamethasone sodium phosphate | \n", - "
| Midostaurin | \n", - "0.224490 | \n", - "CHEBI:63452 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:occu... | \n", - "Midostaurin | \n", - "
| Dasatinib | \n", - "0.214286 | \n", - "CHEBI:49375 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:sensitivity_associate... | \n", - "Dasatinib | \n", - "
| Acetaminophen | \n", - "0.214286 | \n", - "CHEBI:46195 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:affe... | \n", - "Acetaminophen | \n", - "
| Metformin | \n", - "0.214286 | \n", - "CHEBI:6801 | \n", - "biolink:has_adverse_event; biolink:treats_or_a... | \n", - "biolink:affects; biolink:affects; biolink:occu... | \n", - "Metformin | \n", - "
| Alvocidib | \n", - "0.214286 | \n", - "CHEBI:47344 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:occu... | \n", - "Alvocidib | \n", - "
| Sorafenib | \n", - "0.214286 | \n", - "CHEBI:50924 | \n", - "biolink:treats_or_applied_or_studied_to_treat;... | \n", - "biolink:affects; biolink:affects; biolink:occu... | \n", - "Sorafenib | \n", - "
| \n", - " | Doxorubicin | \n", - "Azacitidine | \n", - "Tretinoin | \n", - "Cytarabine | \n", - "Etoposide | \n", - "Midostaurin | \n", - "Decitabine | \n", - "Methotrexate | \n", - "Daunorubicin hydrochloride | \n", - "Quizartinib | \n", - "... | \n", - "LEVITIRACETAM | \n", - "bexmarilimab | \n", - "MEK162 | \n", - "DRUGBANK:DB18980 | \n", - "Minnelide | \n", - "Cobicistat | \n", - "Mivavotinib | \n", - "pinometostat | \n", - "Letermovir | \n", - "LB-100 | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| biolink:causes | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:produced_by | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:location_of | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:has_adverse_event | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:studied_to_treat | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:positively_correlated_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:applied_to_treat | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:predisposes_to_condition | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:disrupts | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:in_clinical_trials_for | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "
| biolink:affects | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:occurs_together_in_literature_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:treats_or_applied_or_studied_to_treat | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "
| biolink:contributes_to | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:subclass_of | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:related_to | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:treats | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:associated_with | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
18 rows × 2906 columns
\n", - "| \n", - " | output_node | \n", - "Name | \n", - "Num_of_primary_infores | \n", - "type_of_nodes | \n", - "unique_predicates | \n", - "
|---|---|---|---|---|---|
| 21 | \n", - "CHEBI:28748 | \n", - "Doxorubicin | \n", - "13 | \n", - "subject | \n", - "[biolink:treats_or_applied_or_studied_to_treat... | \n", - "
| 29 | \n", - "CHEBI:2038 | \n", - "Azacitidine | \n", - "12 | \n", - "subject | \n", - "[biolink:treats_or_applied_or_studied_to_treat... | \n", - "
| 256 | \n", - "CHEBI:15367 | \n", - "Tretinoin | \n", - "11 | \n", - "subject | \n", - "[biolink:affects, biolink:related_to, biolink:... | \n", - "
| 23 | \n", - "CHEBI:28680 | \n", - "Cytarabine | \n", - "11 | \n", - "subject | \n", - "[biolink:treats_or_applied_or_studied_to_treat... | \n", - "
| 20 | \n", - "CHEBI:4911 | \n", - "Etoposide | \n", - "10 | \n", - "subject | \n", - "[biolink:treats_or_applied_or_studied_to_treat... | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 1086 | \n", - "PUBCHEM.COMPOUND:24950485 | \n", - "Cobicistat | \n", - "1 | \n", - "subject | \n", - "[biolink:in_clinical_trials_for] | \n", - "
| 1085 | \n", - "MESH:C000620859 | \n", - "Mivavotinib | \n", - "1 | \n", - "subject | \n", - "[biolink:in_clinical_trials_for] | \n", - "
| 1084 | \n", - "CHEBI:230493 | \n", - "pinometostat | \n", - "1 | \n", - "subject | \n", - "[biolink:in_clinical_trials_for] | \n", - "
| 1083 | \n", - "PUBCHEM.COMPOUND:9916099 | \n", - "Letermovir | \n", - "1 | \n", - "subject | \n", - "[biolink:in_clinical_trials_for] | \n", - "
| 2905 | \n", - "UNII:0C1JUU9S0L | \n", - "LB-100 | \n", - "1 | \n", - "subject | \n", - "[biolink:treats_or_applied_or_studied_to_treat] | \n", - "
2906 rows × 5 columns
\n", - "| \n", - " | BAX | \n", - "BAD | \n", - "MAPK8 | \n", - "MAPK1 | \n", - "BCL2L11 | \n", - "TP53 | \n", - "FKBP8 | \n", - "BCL2L1 | \n", - "BECN1 | \n", - "PMAIP1 | \n", - "... | \n", - "SLC12A9 | \n", - "TCF7L1 | \n", - "ATG3 | \n", - "BIRC8_HUMAN Baculoviral IAP repeat-containing protein 8 (sprot) | \n", - "GATA4 | \n", - "ANXA1 | \n", - "LBX1 | \n", - "PPP1R3C | \n", - "NEUROD1 | \n", - "MMP1 | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| biolink:has_part | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:coexists_with | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:homologous_to | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:close_match | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:affects | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "
| biolink:sensitivity_associated_with | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:regulated_by | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "
| biolink:interacts_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:genetically_interacts_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:correlated_with | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:affected_by | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:resistance_associated_with | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:part_of | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:colocalizes_with | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:subclass_of | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:physically_interacts_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:regulates | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "
| biolink:related_to | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "1 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:directly_physically_interacts_with | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "0 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "1 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
| biolink:produced_by | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "... | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "0 | \n", - "
20 rows × 2650 columns
\n", - "| \n", - " | output_node | \n", - "Name | \n", - "Num_of_primary_infores | \n", - "type_of_nodes | \n", - "unique_predicates | \n", - "
|---|---|---|---|---|---|
| 32 | \n", - "NCBIGene:581 | \n", - "BAX | \n", - "9 | \n", - "subject | \n", - "[biolink:regulates, biolink:physically_interac... | \n", - "
| 20 | \n", - "NCBIGene:572 | \n", - "BAD | \n", - "8 | \n", - "subject | \n", - "[biolink:regulates, biolink:physically_interac... | \n", - "
| 18 | \n", - "NCBIGene:5599 | \n", - "MAPK8 | \n", - "8 | \n", - "subject | \n", - "[biolink:regulates, biolink:physically_interac... | \n", - "
| 16 | \n", - "NCBIGene:5594 | \n", - "MAPK1 | \n", - "8 | \n", - "subject | \n", - "[biolink:regulates, biolink:physically_interac... | \n", - "
| 28 | \n", - "NCBIGene:10018 | \n", - "BCL2L11 | \n", - "8 | \n", - "subject | \n", - "[biolink:regulates, biolink:physically_interac... | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 1080 | \n", - "NCBIGene:301 | \n", - "ANXA1 | \n", - "1 | \n", - "subject | \n", - "[biolink:affects, biolink:regulates, biolink:r... | \n", - "
| 1081 | \n", - "NCBIGene:10660 | \n", - "LBX1 | \n", - "1 | \n", - "subject | \n", - "[biolink:affects, biolink:regulates, biolink:r... | \n", - "
| 1082 | \n", - "NCBIGene:5507 | \n", - "PPP1R3C | \n", - "1 | \n", - "subject | \n", - "[biolink:affects, biolink:regulates, biolink:r... | \n", - "
| 1083 | \n", - "NCBIGene:4760 | \n", - "NEUROD1 | \n", - "1 | \n", - "subject | \n", - "[biolink:affects, biolink:regulated_by, biolin... | \n", - "
| 2649 | \n", - "NCBIGene:4312 | \n", - "MMP1 | \n", - "1 | \n", - "subject | \n", - "[biolink:regulated_by] | \n", - "
2650 rows × 5 columns
\n", - "Nodes: {net.num_nodes()} Edges: {net.num_edges()}
\"\n", - "net.show(\"merged_graph.html\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# put G2 into table format\n", - "import pandas as pd\n", - "edges_data = []\n", - "for u, v, d in G2.edges(data=True):\n", - " edge_info = {\n", - " 'source': u,\n", - " 'target': v,\n", - " }\n", - " edge_info.update(d) # add all edge attributes\n", - " edges_data.append(edge_info)\n", - "\n", - "edges_df = pd.DataFrame(edges_data)\n", - "edges_df.to_csv('BCL2_directly_physically_interacts_with_edges.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# End of the example\n" + "timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')\n", + "with open('TCT_neighborhood_finder_result_'+input_identifiers.replace(':', '_')+'_'+timestamp+'.json', 'w') as f:\n", + " json.dump(TCT_neighborhood_finder_result, f)" ] } ], @@ -1220,7 +256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/notebooks/Neighborhood_finder_multiple_nodes.ipynb b/notebooks/Neighborhood_finder_multiple_nodes.ipynb new file mode 100644 index 0000000..def126b --- /dev/null +++ b/notebooks/Neighborhood_finder_multiple_nodes.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "60c9628e-63ee-42f7-95d2-f1840b321aa3", + "metadata": {}, + "source": [ + "# Neighborhood finder with multiple starting nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f1df4a59-caf9-4477-a789-ad6aba33ba90", + "metadata": {}, + "outputs": [], + "source": [ + "from TCT import node_normalizer\n", + "from TCT import name_resolver\n", + "from TCT import translator_metakg\n", + "from TCT import translator_kpinfo\n", + "from TCT import translator_query\n", + "from TCT import TCT_neighborhood_finder\n", + "from TCT import TCT_network_annotator\n", + "\n", + "from TCT import TCT" + ] + }, + { + "cell_type": "markdown", + "id": "3e033e2c-eb38-406a-ac84-2edb790ec1e5", + "metadata": {}, + "source": [ + "## Load Translator resources" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ab0711e0-bc03-48f2-8282-65bbe9ea30d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipping server without x-maturity: {'url': '/sipr'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n" + ] + } + ], + "source": [ + "APInames, metaKG, Translator_KP_info= translator_metakg.load_translator_resources(use_new_metakg_url=True)\n", + "\n", + "All_predicates = list(set(metaKG['Predicate']))\n", + "All_categories = list((set(list(set(metaKG['Subject']))+list(set(metaKG['Object'])))))\n", + "API_withMetaKG = list(set(metaKG['API']))\n", + "\n", + "API_predicates = {}\n", + "for api in API_withMetaKG:\n", + " API_predicates[api] = list(set(metaKG[metaKG['API'] == api]['Predicate']))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "02fc8d7b", + "metadata": {}, + "outputs": [], + "source": [ + "# This is an example of selecting a list of APIs for the neighborhood finder. The user can modify this list to include the APIs they want to use. The APIs in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. The user can also modify the list of predicates to use for finding the neighborhood. The predicates in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. \n", + "# The user can also modify the list of categories to use for finding the neighborhood. \n", + "# The categories in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph.\n", + "# if selected_APIlist is empty, use all APIs in APInames\n", + "selected_APIlist = [#'Retriever',\n", + " #'Clinical Trials KP - TRAPI 1.5.0',\n", + " #'Drug Approvals KP - TRAPI 1.5.0',\n", + " #'Genetics Data Provider for NCATS Biomedical Translator Reasoners',\n", + " #'Microbiome KP - TRAPI 1.5.0',\n", + " #'MolePro',\n", + " #'COHD TRAPI',\n", + " #'RTX KG2 - TRAPI 1.5.0',\n", + " #'Text Mined Cooccurrence API',\n", + " #'CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0',\n", + " 'CATRAX Pharmacogenomics KP - TRAPI 1.5.0',\n", + " ]\n", + "\n", + "# add Automat API to the selected API list if it is not already in the list\n", + "#for api in APInames:\n", + "# if 'Automat' in api and api not in selected_APIlist:\n", + "# selected_APIlist.append(api)\n", + " \n", + "#selected_APIlist = ['Retriever'] # select just Retriever endpoint\n", + "# select a list of APIs to use and a list of predicates to use\n", + "if len(selected_APIlist) == 0:\n", + " select_APIs = APInames\n", + "else:\n", + " select_APIs = {k: APInames[k] for k in selected_APIlist if k in APInames}\n", + "\n", + "\n", + "selected_metaKG = metaKG[metaKG['API'].isin(select_APIs.keys())]\n", + "#print(select_APIs)\n", + "\n", + "\n", + "All_predicates = list(set(selected_metaKG['Predicate']))\n", + "All_categories = list((set(list(set(selected_metaKG['Subject']))+list(set(selected_metaKG['Object'])))))\n", + "API_withMetaKG = list(set(selected_metaKG['API']))\n", + "API_predicates = {}\n", + "for api in API_withMetaKG:\n", + " API_predicates[api] = list(set(selected_metaKG[selected_metaKG['API'] == api]['Predicate']))" + ] + }, + { + "cell_type": "markdown", + "id": "d1fb3e34-590f-48a1-9383-839dc3eae98f", + "metadata": {}, + "source": [ + "## Run Neighborhood finder with multiple inputs\n", + "\n", + "Here, we will run neighborhood_finder_multiple_inputs with multiple gene inputs, and find all drugs that are connected to any of these genes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b68f54d0-dfa5-4ef1-a2cc-da31bc5f18aa", + "metadata": {}, + "outputs": [], + "source": [ + "IDs = name_resolver.batch_lookup([ 'STAT3', 'MCL1', 'NFKBIA','MKI67','BCL2','BAX','CASP3','STAT5','NFKB1','ATR'], only_taxa='NCBITaxon:9606', biolink_types=['biolink:Gene'])\n", + "#IDs = name_resolver.batch_lookup(['MTORC'], only_taxa='NCBITaxon:9606', biolink_types=['biolink:Gene'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "504cfba0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['NCBIGene:6774',\n", + " 'NCBIGene:4170',\n", + " 'NCBIGene:4792',\n", + " 'NCBIGene:4288',\n", + " 'NCBIGene:596',\n", + " 'NCBIGene:581',\n", + " 'NCBIGene:836',\n", + " 'NCBIGene:6777',\n", + " 'NCBIGene:4790',\n", + " 'NCBIGene:545']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_identifiers = []\n", + "for gene in IDs:\n", + " input_identifiers.append(IDs[gene].curie)\n", + "input_identifiers = ['NCBIGene:6774', 'NCBIGene:4170','NCBIGene:4792','NCBIGene:4288','NCBIGene:596','NCBIGene:581','NCBIGene:836','NCBIGene:6777','NCBIGene:4790','NCBIGene:545']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "45f0ddbb-0368-4a2e-9b19-ed4969a51718", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['NCBIGene:6774', 'NCBIGene:4170', 'NCBIGene:4792', 'NCBIGene:4288', 'NCBIGene:596', 'NCBIGene:581', 'NCBIGene:836', 'NCBIGene:6777', 'NCBIGene:4790', 'NCBIGene:545']\n", + "CATRAX Pharmacogenomics KP - TRAPI 1.5.0: Success!\n" + ] + } + ], + "source": [ + "result, result_parsed = TCT_neighborhood_finder.neighborhood_finder_multiple_inputs(input_identifiers,\n", + " node2_categories = ['biolink:Gene','biolink:Protein'],\n", + " APInames = APInames,\n", + " metaKG = metaKG,\n", + " API_predicates = API_predicates) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8682160f-ee2b-4cb0-925e-1f6dcd9c8264", + "metadata": {}, + "outputs": [], + "source": [ + "# write a result to a json file\n", + "import json\n", + "# add a timestamp to the file name\n", + "import datetime\n", + "# this will create a new file include all the neighbors in the query\n", + "timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')\n", + "with open('./PathFinder_testing_results/'+'TCT_neighborhood_finder_result_'+'_'+timestamp+'.json', 'w') as f:\n", + " json.dump(result_parsed, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "600eec1f", + "metadata": {}, + "outputs": [], + "source": [ + "# This will create a new file only include the intermediate nodes with degree > 1 or input nodes in the query, and the edges connecting to them.\n", + "result = TCT_network_annotator.get_connected_graph(result_parsed, input_identifiers)\n", + "\n", + "import datetime\n", + "import json\n", + "timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')\n", + "with open('./PathFinder_testing_results/'+'TCT_neighborhood_finder_result_'+'_'+timestamp+'.json', 'w') as f:\n", + " json.dump(result, f)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "my-rdkit-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Network_finder.ipynb b/notebooks/Network_finder.ipynb index f79cff3..46a2d34 100644 --- a/notebooks/Network_finder.ipynb +++ b/notebooks/Network_finder.ipynb @@ -11,14 +11,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "\n", + "import sys\n", + "import os\n", + "sys.path.append('../TCT/')\n", + "from TCT import node_normalizer\n", "from TCT import name_resolver\n", "from TCT import translator_metakg\n", "from TCT import translator_kpinfo\n", "from TCT import translator_query\n", + "from TCT import TCT_neighborhood_finder\n", + "from TCT import TCT_network_annotator\n", + "\n", "from TCT import TCT\n", "\n", "import pandas as pd\n", @@ -28,92 +36,21 @@ }, { "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "def load_translator_resources():\n", - " \"\"\"\n", - " Load the necessary resources for the Translator.\n", - " \"\"\"\n", - " Translator_KP_info,APInames= translator_kpinfo.get_translator_kp_info()\n", - " metaKG = translator_metakg.get_KP_metadata(APInames) \n", - " APInames,metaKG = translator_metakg.add_plover_API(APInames, metaKG)\n", - " return APInames, metaKG, Translator_KP_info\n", - "\n", - "\n", - "def visualize_interaction_network(result_json):\n", - " \"\"\"\n", - " Visualize the interaction network from the result JSON.\n", - "\n", - " Parameters:\n", - " result_json (dict): The result JSON containing subjects, objects, and predicates.\n", - " \n", - " \"\"\"\n", - "\n", - " subjects = []\n", - " objects = []\n", - " predicates = []\n", - "\n", - " for k, v in result_json.items():\n", - " subjects.append(v['subject'])\n", - " objects.append(v['object'])\n", - " predicates.append(v['predicate'])\n", - "\n", - " # Convert subject and object CURIEs to names using lookup\n", - " subject_names = []\n", - " object_names = []\n", - "\n", - " # Use batch_lookup for all subjects and objects to minimize API calls\n", - " unique_nodes = set(subjects + objects)\n", - " node_info_dict = name_resolver.batch_lookup(list(unique_nodes))\n", - " print(node_info_dict)\n", - "\n", - " for subj, obj in zip(subjects, objects):\n", - " subj_info = node_info_dict.get(subj)\n", - " obj_info = node_info_dict.get(obj)\n", - " subject_names.append(subj_info.name if subj_info and hasattr(subj_info, 'name') else subj)\n", - " object_names.append(obj_info.name if obj_info and hasattr(obj_info, 'name') else obj)\n", - "\n", - "\n", - " df = pd.DataFrame({\n", - " \"Subject\": subject_names,\n", - " \"Object\": object_names,\n", - " \"Predicate\": predicates,\n", - " })\n", - "\n", - " print(df.head())\n", - " # merge predicates and sources\n", - " # Convert 'Sources' to a string representation\n", - " # Remove duplicate edges\n", - " df = df.drop_duplicates()\n", - "\n", - " # Build networkx graph\n", - " G1 = nx.from_pandas_edgelist(df, source='Subject', target='Object', edge_attr=['Predicate'], create_using=nx.MultiGraph)\n", - "\n", - " # Cytoscape style\n", - " graph_style = [\n", - " {'selector': 'node', 'style': {'background-color': 'lightblue', 'shape': 'ellipse', 'label': 'data(id)', 'font-size': '12px'}},\n", - " {'selector': 'edge', 'style': {'label': 'data(Predicate)', 'font-size': '10px', \"curve-style\": \"bezier\"}}\n", - " ]\n", - "\n", - " cyto_graph = ipycytoscape.CytoscapeWidget()\n", - " cyto_graph.graph.add_graph_from_networkx(G1, directed=True)\n", - " cyto_graph.set_style(graph_style)\n", - " cyto_graph.set_layout(name='cose', nodeSpacing=80, edgeLengthVal=50)\n", - "\n", - " display(cyto_graph)\n", - "\n", - " return df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 91, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'url': '/sipr'}\n" + ] + } + ], "source": [ - "APInames, metaKG, Translator_KP_info= load_translator_resources()\n", + "APInames, metaKG, Translator_KP_info= translator_metakg.load_translator_resources(use_new_metakg_url=True)\n", "\n", "All_predicates = list(set(metaKG['Predicate']))\n", "All_categories = list((set(list(set(metaKG['Subject']))+list(set(metaKG['Object'])))))\n", @@ -127,190 +64,142 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "all relevant predicates in Translator:\n", - "['biolink:coexists_with', 'biolink:has_participant', 'biolink:disrupts', 'biolink:same_as', 'biolink:acts_upstream_of_or_within', 'biolink:treats_or_applied_or_studied_to_treat', 'biolink:similar_to', 'biolink:chemically_similar_to', 'biolink:correlated_with', 'biolink:response_affected_by', 'biolink:affects_response_to', 'biolink:genetically_associated_with', 'biolink:negatively_correlated_with', 'biolink:decreases_response_to', 'biolink:related_to', 'biolink:physical_interacts_with', 'biolink:overlaps', 'biolink:transcribed_from', 'biolink:has_gene_product', 'biolink:GeneAffectsChemicalAssociation', 'biolink:associated_with_increased_likelihood_of', 'biolink:interacts_with', 'biolink:part_of', 'biolink:orthologous_to', 'biolink:affects', 'biolink:gene_product_of', 'biolink:physically_interacts_with', 'biolink:subclass_of', 'biolink:derives_into', 'biolink:preventative_for_condition', 'biolink:regulated_by', 'biolink:contraindicated_in', 'biolink:causes', 'biolink:derives_from', 'biolink:genetically_interacts_with', 'biolink:expressed_in', 'biolink:associated_with_sensitivity_to', 'biolink:associated_with_resistance_to', 'biolink:occurs_in', 'biolink:affected_by', 'biolink:regulates', 'biolink:colocalizes_with', 'biolink:has_part', 'biolink:translates_to', 'biolink:in_complex_with', 'biolink:associated_with', 'biolink:occurs_together_in_literature_with', 'biolink:directly_physically_interacts_with', 'biolink:has_member', 'biolink:composed_primarily_of', 'biolink:produces', 'biolink:homologous_to', 'biolink:located_in', 'biolink:produced_by', 'biolink:gene_associated_with_condition', 'biolink:capable_of', 'biolink:biomarker_for', 'biolink:increases_response_to', 'biolink:has_phenotype', 'biolink:is_sequence_variant_of', 'biolink:close_match', 'biolink:positively_correlated_with', 'biolink:target_for', 'biolink:has_input', 'biolink:coexpressed_with']\n", - "all relevant APIs in Translator:\n", - "['Service Provider TRAPI', 'Automat-pharos(Trapi v1.5.0)', 'RTX KG2 - TRAPI 1.5.0', 'Automat-ehr-clinical-connections-kp(Trapi v1.5.0)', 'MolePro', 'SPOKE KP for TRAPI 1.5', 'Automat-icees-kg(Trapi v1.5.0)', 'Microbiome KP - TRAPI 1.5.0', 'Automat-robokop(Trapi v1.5.0)', 'CATRAX Pharmacogenomics KP - TRAPI 1.5.0', 'Connections Hypothesis Provider API', 'Multiomics KP - TRAPI 1.5.0', 'Automat-ehr-may-treat-kp(Trapi v1.5.0)', 'Text Mined Cooccurrence API', 'Automat-genome-alliance(Trapi v1.5.0)', 'Automat-hetionet(Trapi v1.5.0)', 'Automat-cam-kp(Trapi v1.5.0)', 'BioThings Explorer (BTE) TRAPI', 'CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0', 'Automat-monarchinitiative(Trapi v1.5.0)', 'imProving Agent for TRAPI 1.5']\n", - "21\n" + "(4266, 5)\n" ] - }, - { - "data": { - "text/plain": [ - "['https://bte.transltr.io/v1/team/Service%20Provider/query/',\n", - " 'https://automat.transltr.io/pharos/query/',\n", - " 'https://kg2cploverdb.ci.transltr.io/kg2c/query',\n", - " 'https://automat.renci.org/ehr-clinical-connections-kp/query/',\n", - " 'https://molepro-trapi.transltr.io/molepro/trapi/v1.5/query/',\n", - " 'https://spokekp.transltr.io/api/v1.5/query/',\n", - " 'https://automat.transltr.io/icees-kg/query/',\n", - " 'https://multiomics.rtx.ai:9990/mbkp/query',\n", - " 'https://automat.transltr.io/robokopkg/query/',\n", - " 'https://multiomics.rtx.ai:9990/PharmacogenomicsKG/query',\n", - " 'https://chp-api.transltr.io/query/',\n", - " 'https://multiomics.rtx.ai:9990/multiomics/query',\n", - " 'https://automat.renci.org/ehr-may-treat-kp/query/',\n", - " 'https://cooccurrence.transltr.io/query/',\n", - " 'https://automat.transltr.io/genome-alliance/query/',\n", - " 'https://automat.transltr.io/hetio/query/',\n", - " 'https://automat.transltr.io/cam-kp/query/',\n", - " 'https://bte.transltr.io/v1/query/',\n", - " 'https://multiomics.rtx.ai:9990/BigGIM_DrugResponse_PerformancePhase/query',\n", - " 'https://automat.transltr.io/monarch-kg/query/',\n", - " 'https://ia.transltr.io/api/v1.5/query/']" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "# input_nodes\n", - "input_list = ['NPM1', 'FLT3', 'NRAS', 'BCL2', 'WNT7B']\n", - "input_info = name_resolver.batch_lookup(input_list)\n", - "input_node1_list = [] # get the curie of the nodes in the input_info dictionary\n", - "input_node1_list = [input_info[node].curie for node in input_list]\n", - "input_node1_category = []\n", - "for node in input_list:\n", - " if hasattr(input_info[node], 'types'):\n", - " input_node1_category = input_node1_category + input_info[node].types\n", - "input_node1_category = list(set(input_node1_category))\n", - "\n", - "# query nodes\n", - "input_node2_list = []\n", - "input_node2_category = ['biolink:Gene', 'biolink:Protein', 'biolink:ChemicalSubstance', 'biolink:Drug', 'biolink:DiseaseOrPhenotypicFeature']\n", - "\n", - "sele_predicates = list(set(TCT.select_concept(sub_list=input_node1_category,obj_list=input_node2_category,metaKG=metaKG)))\n", - "print(\"all relevant predicates in Translator:\")\n", - "print(sele_predicates)\n", - "\n", - "\n", - "# Get all APIs for the input node1 and node2, user can furter select the APIs among this list\n", - "sele_APIs = TCT.select_API(sub_list=input_node1_category,obj_list=input_node2_category,metaKG=metaKG)\n", - "print(\"all relevant APIs in Translator:\")\n", - "print(sele_APIs)\n", - "print(len(sele_APIs))\n", - "\n", - "#sele_APIs = ['Multiomics BigGIM-DrugResponse KP API']\n", - "sele_APIs = sele_APIs\n", - "# get API URLs : two options: filter by API names or filter by predicates\n", - "API_URLs = TCT.get_Translator_API_URL(sele_APIs, \n", - " APInames)\n", - "\n", - "API_URLs" + "# This is an example of selecting a list of APIs for the neighborhood finder. The user can modify this list to include the APIs they want to use. The APIs in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. The user can also modify the list of predicates to use for finding the neighborhood. The predicates in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. \n", + "# The user can also modify the list of categories to use for finding the neighborhood. \n", + "# The categories in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph.\n", + "# if selected_APIlist is empty, use all APIs in APInames\n", + "selected_APIlist = ['Retriever',\n", + " 'Clinical Trials KP - TRAPI 1.5.0',\n", + " 'Drug Approvals KP - TRAPI 1.5.0',\n", + " 'Genetics Data Provider for NCATS Biomedical Translator Reasoners',\n", + " 'Microbiome KP - TRAPI 1.5.0',\n", + " 'MolePro',\n", + " 'COHD TRAPI',\n", + " 'RTX KG2 - TRAPI 1.5.0',\n", + " 'Text Mined Cooccurrence API',\n", + " 'CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0',\n", + " 'CATRAX Pharmacogenomics KP - TRAPI 1.5.0',\n", + " ]\n", + "\n", + "# add Automat API to the selected API list if it is not already in the list\n", + "#for api in APInames:\n", + "# if 'Automat' in api and api not in selected_APIlist:\n", + " # selected_APIlist.append(api)\n", + "\n", + "selected_APIlist = [ 'Retriever']\n", + "\n", + " \n", + "# select a list of APIs to use and a list of predicates to use\n", + "if len(selected_APIlist) == 0:\n", + " select_APIs = APInames\n", + "else:\n", + " select_APIs = {k: APInames[k] for k in selected_APIlist if k in APInames}\n", + "\n", + "selected_metaKG = metaKG[metaKG['API'].isin(select_APIs.keys())]\n", + "#print(select_APIs)\n", + "print(selected_metaKG.shape)\n", + "\n", + "All_predicates = list(set(selected_metaKG['Predicate']))\n", + "All_categories = list((set(list(set(selected_metaKG['Subject']))+list(set(selected_metaKG['Object'])))))\n", + "API_withMetaKG = list(set(selected_metaKG['API']))\n", + "API_predicates = {}\n", + "for api in API_withMetaKG:\n", + " API_predicates[api] = list(set(selected_metaKG[selected_metaKG['API'] == api]['Predicate']))" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "query_json = TCT.format_query_json(input_node1_list, # a list of identifiers for input node1\n", - " input_node2_list, # it can be empty list if only want to query node1\n", - " input_node1_category, # a list of categories of input node1\n", - " input_node2_category, # a list of categories of input node2\n", - " sele_predicates) # a list of pre" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'Automat-ehr-clinical-connections-kp(Trapi v1.5.0)' generated an exception: argument of type 'NoneType' is not iterable\n", - "Microbiome KP - TRAPI 1.5.0: Success!\n", - "'Automat-ehr-may-treat-kp(Trapi v1.5.0)' generated an exception: argument of type 'NoneType' is not iterable\n", - "'imProving Agent for TRAPI 1.5' generated an exception: argument of type 'NoneType' is not iterable\n", - "'SPOKE KP for TRAPI 1.5' generated an exception: argument of type 'NoneType' is not iterable\n", - "'Automat-icees-kg(Trapi v1.5.0)' generated an exception: argument of type 'NoneType' is not iterable\n", - "'Automat-monarchinitiative(Trapi v1.5.0)' generated an exception: argument of type 'NoneType' is not iterable\n", - "'MolePro' generated an exception: argument of type 'NoneType' is not iterable\n", - "'Multiomics KP - TRAPI 1.5.0' generated an exception: argument of type 'NoneType' is not iterable\n", - "'Text Mined Cooccurrence API' generated an exception: argument of type 'NoneType' is not iterable\n", - "Automat-genome-alliance(Trapi v1.5.0): Success!\n", - "'Connections Hypothesis Provider API' generated an exception: 'NoneType' object has no attribute 'get'\n", - "Automat-cam-kp(Trapi v1.5.0): Success!\n", - "CATRAX Pharmacogenomics KP - TRAPI 1.5.0: Success!\n", - "CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0: Success!\n", - "Automat-hetionet(Trapi v1.5.0): Success!\n", - "Automat-pharos(Trapi v1.5.0): Success!\n", - "RTX KG2 - TRAPI 1.5.0: Success!\n", - "Automat-robokop(Trapi v1.5.0): Success!\n", - "Service Provider TRAPI: Success!\n", - "BioThings Explorer (BTE) TRAPI: Success!\n" - ] - } - ], - "source": [ - "# Step 5: Query Translator APIs and parse results\n", - "\n", - "result = translator_query.parallel_api_query(query_json=query_json, \n", - " select_APIs= sele_APIs, \n", - " APInames=APInames,\n", - " API_predicates=API_predicates,\n", - " max_workers=len(sele_APIs))" + "# input_nodes\n", + "input_list = ['STAT3', 'TP53', 'MCL1', 'NFKBIA','MKI67','BCL2L11','BCL2L1','BCL2','BAX','BAK1','BAD','CASP3','STAT5','MAPK14','NFKB1','ATR','NRAS','KRAS','MAPK1','MAPK2']\n", + "#input_list = ['NPM1','HOXA9','FLT3','IDH1','RUNX1']\n" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'NCBIGene:596': TranslatorNode(curie='UniProtKB:A0A4X1SN03', label='A0A4X1SN03_PIG Zinc finger protein 596 (trembl)', types=['biolink:Protein', 'biolink:GeneProductMixin', 'biolink:Polypeptide', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:ChemicalEntityOrProteinOrPolypeptide', 'biolink:BiologicalEntity', 'biolink:ThingWithTaxon', 'biolink:NamedThing', 'biolink:Entity', 'biolink:GeneOrGeneProduct', 'biolink:MacromolecularMachineMixin'], synonyms=None, curie_synonyms=None), 'NCBIGene:4869': TranslatorNode(curie='PUBCHEM.COMPOUND:676542', label='Cl-4869 peak 2', types=['biolink:SmallMolecule', 'biolink:MolecularEntity', 'biolink:ChemicalEntity', 'biolink:PhysicalEssence', 'biolink:ChemicalOrDrugOrTreatment', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:ChemicalEntityOrProteinOrPolypeptide', 'biolink:NamedThing', 'biolink:Entity', 'biolink:PhysicalEssenceOrOccurrent'], synonyms=None, curie_synonyms=None), 'NCBIGene:4893': TranslatorNode(curie='MESH:C568585', label='S-4893 compound', types=['biolink:ChemicalEntity', 'biolink:PhysicalEssence', 'biolink:ChemicalOrDrugOrTreatment', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:ChemicalEntityOrProteinOrPolypeptide', 'biolink:NamedThing', 'biolink:Entity', 'biolink:PhysicalEssenceOrOccurrent'], synonyms=None, curie_synonyms=None), 'NCBIGene:2322': TranslatorNode(curie='NCBIGene:100313282', label='MIR2322', types=['biolink:Gene', 'biolink:GeneOrGeneProduct', 'biolink:GenomicEntity', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:PhysicalEssence', 'biolink:OntologyClass', 'biolink:BiologicalEntity', 'biolink:ThingWithTaxon', 'biolink:NamedThing', 'biolink:Entity', 'biolink:PhysicalEssenceOrOccurrent', 'biolink:MacromolecularMachineMixin'], synonyms=None, curie_synonyms=None)}\n", - " Subject Object Predicate\n", - "0 NCBIGene:2322 NCBIGene:4893 biolink:associated_with\n", - "1 NCBIGene:4893 NCBIGene:2322 biolink:associated_with\n", - "2 NCBIGene:2322 NCBIGene:2322 biolink:regulates\n", - "3 NCBIGene:2322 NCBIGene:2322 biolink:physically_interacts_with\n", - "4 NCBIGene:596 NCBIGene:596 biolink:physically_interacts_with\n" + "NCBIGene:6774\n", + "Retriever: Success!\n", + "ENSEMBL:ENSG00000255346: no preferred name\n", + "NodeNorm does not know about these identifiers: PR:000085869,PR:000086002\n", + "NCBIGene:7157\n", + "Retriever: Success!\n", + "NCBIGene:4170\n", + "Retriever: Success!\n", + "NCBIGene:4792\n", + "Retriever: Success!\n", + "NCBIGene:4288\n", + "Retriever: Success!\n", + "NCBIGene:10018\n", + "Retriever: Success!\n", + "NCBIGene:598\n", + "Retriever: Success!\n", + "NCBIGene:596\n", + "Retriever: Success!\n", + "NCBIGene:581\n", + "Retriever: Success!\n", + "NCBIGene:578\n", + "Retriever: Success!\n", + "NCBIGene:572\n", + "Retriever: Success!\n", + "NCBIGene:836\n", + "Retriever: Success!\n", + "NCBIGene:6777\n", + "Retriever: Success!\n", + "NCBIGene:1432\n", + "Retriever: Success!\n", + "NodeNorm does not know about these identifiers: UniProtKB:C0HMD6\n", + "NCBIGene:4790\n", + "Retriever: Success!\n", + "NCBIGene:545\n", + "Retriever: Success!\n", + "NCBIGene:4893\n", + "Retriever: Success!\n", + "NCBIGene:3845\n", + "Retriever: Success!\n", + "NCBIGene:5594\n", + "Retriever: Success!\n", + "NodeNorm does not know about these identifiers: UniProtKB:C0HMD6\n", + "NCBIGene:5594\n", + "Retriever: Success!\n", + "NodeNorm does not know about these identifiers: UniProtKB:C0HMD6\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "47073cd47e494b05b9875a0906f3dffb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "CytoscapeWidget(cytoscape_layout={'name': 'cose', 'nodeSpacing': 80, 'edgeLengthVal': 50}, cytoscape_style=[{'…" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "# select the recored in the result which is a dictionary if the subject and objects are both in input_node1_list\n", - "result_filtered = {k: v for k, v in result.items() if isinstance(v, dict) and\n", - " v.get('subject') in input_node1_list and\n", - " v.get('object') in input_node1_list}\n", - "\n", - "len(result_filtered)\n", - "result_df =visualize_interaction_network(result_filtered)\n" + "network_anno = TCT_network_annotator.network_annotator(gene_list = input_list,\n", + " select_APIs = select_APIs,\n", + " node2_categories = ['biolink:Gene','biolink:Protein'],\n", + " select_metaKG = selected_metaKG,\n", + " API_predicates = API_predicates,\n", + " output_file = 'network_anno.json')" ] } ], "metadata": { "kernelspec": { - "display_name": "3.12.1", + "display_name": "my-rdkit-env", "language": "python", "name": "python3" }, @@ -324,7 +213,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/notebooks/Path_finder.ipynb b/notebooks/Path_finder.ipynb index 401e7a5..ae2c584 100644 --- a/notebooks/Path_finder.ipynb +++ b/notebooks/Path_finder.ipynb @@ -1,18 +1,11 @@ { "cells": [ { - "attachments": { - "3da53daa-b927-420f-8b64-97ee4046cc60.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ "### Path Finder: What are the potential paths between two nodes?\n", - "### This pipeline can be used to get a ranked path between A and B given a set of paths.\n", - "\n", - "\n" + "### This pipeline can be used to get a ranked path between A and B given a set of paths." ] }, { @@ -21,32 +14,35 @@ "metadata": {}, "outputs": [], "source": [ - "from TCT import TCT\n", - "\n", + "import sys\n", + "import os\n", + "sys.path.append('../TCT/')\n", + "import TCT\n", + "from TCT import translator_kpinfo\n", + "from TCT import node_normalizer\n", "from TCT import name_resolver\n", "from TCT import translator_metakg\n", - "from TCT import translator_kpinfo\n", "from TCT import translator_query\n", + "from TCT import TCT_pathfinder\n", "\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import requests\n", - "import json\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import networkx as nx\n", - "import numpy as np\n", - "import ipycytoscape\n", - "import yaml\n" + "import requests" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipping server without x-maturity: {'url': '/sipr'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n" + ] + } + ], "source": [ "APInames, metaKG, Translator_KP_info= translator_metakg.load_translator_resources()\n", "\n", @@ -69,14 +65,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "(22135, 5)\n" + "(16588, 5)\n" ] } ], "source": [ - "# select a list of APIs to use and a list of predicates to use\n", - "selected_APIlist = []\n", + "# This is an example of selecting a list of APIs for the neighborhood finder. The user can modify this list to include the APIs they want to use. The APIs in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. The user can also modify the list of predicates to use for finding the neighborhood. The predicates in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph. \n", + "# The user can also modify the list of categories to use for finding the neighborhood. \n", + "# The categories in this list are the ones that will be used to find the neighborhood of a given node in the knowledge graph.\n", + "# if selected_APIlist is empty, use all APIs in APInames\n", + "selected_APIlist = ['Retriever',\n", + " 'Clinical Trials KP - TRAPI 1.5.0',\n", + " 'Drug Approvals KP - TRAPI 1.5.0',\n", + " 'Genetics Data Provider for NCATS Biomedical Translator Reasoners',\n", + " 'Microbiome KP - TRAPI 1.5.0',\n", + " 'MolePro',\n", + " 'COHD TRAPI',\n", + " 'RTX KG2 - TRAPI 1.5.0',\n", + " 'Text Mined Cooccurrence API',\n", + " 'CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0',\n", + " 'CATRAX Pharmacogenomics KP - TRAPI 1.5.0',\n", + " ]\n", + "\n", + "# add Automat API to the selected API list if it is not already in the list\n", + "for api in APInames:\n", + " if 'Automat' in api and api not in selected_APIlist:\n", + " selected_APIlist.append(api)\n", "\n", + "#selected_APIlist = ['Retriever']\n", + "\n", + "# select a list of APIs to use and a list of predicates to use\n", "if len(selected_APIlist) == 0:\n", " select_APIs = APInames\n", "else:\n", @@ -84,84 +102,90 @@ "\n", "selected_metaKG = metaKG[metaKG['API'].isin(select_APIs.keys())]\n", "#print(select_APIs)\n", - "print(selected_metaKG.shape)\n" + "print(selected_metaKG.shape)\n", + "\n", + "All_predicates = list(set(selected_metaKG['Predicate']))\n", + "All_categories = list((set(list(set(selected_metaKG['Subject']))+list(set(selected_metaKG['Object'])))))\n", + "API_withMetaKG = list(set(selected_metaKG['API']))\n", + "API_predicates = {}\n", + "for api in API_withMetaKG:\n", + " API_predicates[api] = list(set(selected_metaKG[selected_metaKG['API'] == api]['Predicate']))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "#name_resolver.lookup('miR-155', only_taxa='NCBITaxon:9606')\n", + "subject_name = 'Revumenib'\n", + "subject_node = name_resolver.lookup(subject_name).curie\n", + "subject_category = name_resolver.lookup(subject_name).types\n", + "subject_category = [\"biolink:Drug\", \"biolink:SmallMolecule\", \"biolink:ChemicalSubstance\"]\n", + "\n", + "object_name = 'acute myeloid leukemia'\n", + "object_node = name_resolver.lookup(object_name, biolink_category='biolink:Disease').curie\n", + "object_category = name_resolver.lookup(object_name, biolink_category='biolink:Disease').types\n", + "object_category = [\"biolink:Disease\"]\n", + "\n", + "intermediate_categories = [ 'biolink:Gene','biolink:Protein']\n", + "\n", + "#name_resolver.lookup('Myelodysplastic syndrome', return_top_response=False, biolink_category='biolink:Disease')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "TranslatorNode(curie='NCBIGene:406947', label='MIR155', types=['biolink:Gene', 'biolink:GeneOrGeneProduct', 'biolink:GenomicEntity', 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'biolink:PhysicalEssence', 'biolink:OntologyClass', 'biolink:BiologicalEntity', 'biolink:ThingWithTaxon', 'biolink:NamedThing', 'biolink:Entity', 'biolink:PhysicalEssenceOrOccurrent', 'biolink:MacromolecularMachineMixin'], synonyms=None, curie_synonyms=None)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "PUBCHEM.COMPOUND:132212657 MONDO:0018874\n" + ] } ], "source": [ - "name_resolver.lookup('miR-155', only_taxa='NCBITaxon:9606')" + "print(subject_node , object_node)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NCBIGene:3458\n", - "MONDO:0100096\n", - "CATRAX Pharmacogenomics KP - TRAPI 1.5.0: Success!\n", - "Automat-genome-alliance(Trapi v1.5.0): Success!\n", - "CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0: Success!\n", - "Automat-cam-kp(Trapi v1.5.0): Success!\n", - "Automat-hetionet(Trapi v1.5.0): Success!\n", + "PUBCHEM.COMPOUND:132212657\n", + "MONDO:0018874\n", "RTX KG2 - TRAPI 1.5.0: Success!\n", - "Automat-robokop(Trapi v1.5.0): Success!\n", - "Service Provider TRAPI: Success!\n", - "BioThings Explorer (BTE) TRAPI: Success!\n", + "MolePro: Success!\n", + "Automat-monarchinitiative(Trapi v1.5.0): Success!\n", "CATRAX Pharmacogenomics KP - TRAPI 1.5.0: Success!\n", + "Genetics Data Provider for NCATS Biomedical Translator Reasoners: Success!\n", "Automat-pharos(Trapi v1.5.0): Success!\n", "Automat-robokop(Trapi v1.5.0): Success!\n", + "CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0: Success!\n", "Clinical Trials KP - TRAPI 1.5.0: Success!\n", - "Microbiome KP - TRAPI 1.5.0: Success!\n", "RTX KG2 - TRAPI 1.5.0: Success!\n", - "Service Provider TRAPI: Success!\n", - "BioThings Explorer (BTE) TRAPI: Success!\n", - "UniProtKB:P12544-1: no preferred name\n", - "ENSEMBL:ENSP00000497585: no preferred name\n", - "ENSEMBL:ENSP00000497585: no preferred name\n", - "UniProtKB:P12544-1: no preferred name\n", - "UniProtKB:Q92614-1: no preferred name\n", - "UniProtKB:Q92614-1: no preferred name\n", - "NCBIGene:3497: no preferred name\n", - "NodeNorm does not know about these identifiers: UniProtKB:Q8NF90,UniProtKB:Q8WWJ7\n", - "NCBIGene:1055: no preferred name\n", - "NCBIGene:28755: no preferred name\n", - "NCBIGene:3497: no preferred name\n", - "NodeNorm does not know about these identifiers: UMLS:C5943250,UMLS:C5959582,UMLS:C5908975,UMLS:C5926549,UMLS:C5943245,CHEMBL.TARGET:CHEMBL1697664,CHEMBL.TARGET:CHEMBL6188,CHEMBL.TARGET:CHEMBL4295579,CHEMBL.TARGET:CHEMBL1075031,CHEMBL.TARGET:CHEMBL4295609,CHEMBL.TARGET:CHEMBL1287621,CHEMBL.TARGET:CHEMBL3499,CHEMBL.TARGET:CHEMBL4295625,CHEMBL.TARGET:CHEMBL4295542,CHEMBL.TARGET:CHEMBL1741198,CHEMBL.TARGET:CHEMBL3309030,CHEMBL.TARGET:CHEMBL4882,CHEMBL.TARGET:CHEMBL3309037,NCBIGene:619501,NCBIGene:100187907,NCBIGene:260431,NCBIGene:100188881,NCBIGene:2656,NCBIGene:100616100,NCBIGene:7808,NCBIGene:100187761,NCBIGene:7909,NCBIGene:28891,NCBIGene:338340,NCBIGene:100616102,NCBIGene:7864,NCBIGene:100862683,NCBIGene:100616101,NCBIGene:105376906,NCBIGene:100862684,NCBIGene:619465,NCBIGene:100616103,NCBIGene:100775105,NCBIGene:100862688,NCBIGene:8168,NCBIGene:7502,NCBIGene:NaN,NCBIGene:470,NCBIGene:1483,NCBIGene:28874,NCBIGene:3438,NCBIGene:4827,NCBIGene:100862695,NCBIGene:8129,NCBIGene:4397,NCBIGene:100862689,NCBIGene:100862690,ENSEMBL:ENSP00000344192,ZFIN:ZDB-GENE-081104-23,RGD:6492449,WB:WBGene00002976,WB:WBGene00000039,WB:WBGene00005643,WB:WBGene00005647,WB:WBGene00005648,WB:WBGene00005649,WB:WBGene00012084,WB:WBGene00013969,WB:WBGene00009278,WB:WBGene00001501,WB:WBGene00002881,WB:WBGene00001500,WB:WBGene00004249,WB:WBGene00006588,WB:WBGene00008996,WB:WBGene00016283,WB:WBGene00077763,WB:WBGene00006587,WB:WBGene00006589,WB:WBGene00009108,WB:WBGene00010423,RGD:2320097,RGD:1597199,RGD:9404643,NCBIGene:70044589,NCBIGene:87307421,NCBIGene:69839598,NCBIGene:77093917,NCBIGene:43014149,NCBIGene:9734648,NCBIGene:64304292,NCBIGene:66845091,NCBIGene:66674871,NCBIGene:69877904,NCBIGene:69447949,NCBIGene:77268409,NCBIGene:72435423,NCBIGene:66412180,NCBIGene:56333716,NCBIGene:86836226,NCBIGene:69526498,NCBIGene:56943405,NCBIGene:63019226,NCBIGene:91433784,NCBIGene:69735768,NCBIGene:69731355,NCBIGene:66967904,NCBIGene:50043243,NCBIGene:90938815,NCBIGene:57312032,NCBIGene:87309862,NCBIGene:66423232,NCBIGene:66545538,NCBIGene:69880493,NCBIGene:56280614,NCBIGene:66670882,NCBIGene:67491618,NCBIGene:56596593,NCBIGene:60545508,NCBIGene:66716715,NCBIGene:89615363,NCBIGene:70085494,NCBIGene:66216181,NCBIGene:75175404,NCBIGene:89614415,NCBIGene:69660549,NCBIGene:57633102,NCBIGene:69988122,NCBIGene:69796008,NCBIGene:89528680,NCBIGene:69619515,NCBIGene:69886013,NCBIGene:87008972,NCBIGene:56675691,NCBIGene:72406518,NCBIGene:72495708,NCBIGene:79235574,NCBIGene:84658007,NCBIGene:88770584,NCBIGene:68688299,NCBIGene:69423538,NCBIGene:60780364,NCBIGene:56651995,NCBIGene:61466620,NCBIGene:90616937,NCBIGene:57314851,NCBIGene:77311177,NCBIGene:72405345,NCBIGene:90616400\n", - "Number of possible paths: 897\n", - "NCBIGene:3497: no preferred name\n" + "Number of possible paths: 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/yzhang/Translator_component_toolkit/TCT/TCT.py:1631: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/Users/gqin/Github_repo/Translator_component_toolkit/TCT/TCT.py:1653: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\", fontsize=fontsize)\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "| \n", - " | predicate | \n", - "subject | \n", - "object | \n", - "attributes | \n", - "sources | \n", - "
|---|---|---|---|---|---|
| 0 | \n", - "biolink:interacts_with | \n", - "NCBIGene:169355 | \n", - "NCBIGene:3458 | \n", - "[{'attribute_type_id': 'biolink:knowledge_leve... | \n", - "[{'resource_id': 'infores:string', 'resource_r... | \n", - "
| 1 | \n", - "biolink:interacts_with | \n", - "NCBIGene:169355 | \n", - "NCBIGene:3458 | \n", - "[{'attribute_type_id': 'biolink:knowledge_leve... | \n", - "[{'resource_id': 'infores:string', 'resource_r... | \n", - "
| \n", - " | score | \n", - "output_node | \n", - "predictes1 | \n", - "predictes2 | \n", - "output_node_name | \n", - "
|---|---|---|---|---|---|
| TNF | \n", - "0.400000 | \n", - "NCBIGene:7124 | \n", - "biolink:coexpressed_with; biolink:regulates; b... | \n", - "biolink:genetically_associated_with; biolink:g... | \n", - "TNF | \n", - "
| RSAD2 | \n", - "0.300000 | \n", - "NCBIGene:91543 | \n", - "biolink:coexpressed_with; biolink:affects; bio... | \n", - "biolink:associated_with; biolink:gene_associat... | \n", - "RSAD2 | \n", - "
| ICAM1 | \n", - "0.266667 | \n", - "NCBIGene:3383 | \n", - "biolink:coexpressed_with; biolink:physically_i... | \n", - "biolink:gene_associated_with_condition; biolin... | \n", - "ICAM1 | \n", - "
| CD4 | \n", - "0.266667 | \n", - "NCBIGene:920 | \n", - "biolink:coexpressed_with; biolink:affected_by;... | \n", - "biolink:genetically_associated_with; biolink:r... | \n", - "CD4 | \n", - "
| IRF1 | \n", - "0.266667 | \n", - "NCBIGene:3659 | \n", - "biolink:coexpressed_with; biolink:physically_i... | \n", - "biolink:gene_associated_with_condition; biolin... | \n", - "IRF1 | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| IL26 | \n", - "0.033333 | \n", - "NCBIGene:55801 | \n", - "biolink:coexpressed_with | \n", - "biolink:gene_associated_with_condition | \n", - "IL26 | \n", - "
| CLDN5 | \n", - "0.033333 | \n", - "NCBIGene:7122 | \n", - "biolink:coexpressed_with | \n", - "biolink:gene_associated_with_condition | \n", - "CLDN5 | \n", - "
| OXT | \n", - "0.033333 | \n", - "NCBIGene:5020 | \n", - "biolink:affects; biolink:regulates | \n", - "biolink:gene_associated_with_condition | \n", - "OXT | \n", - "
| MS4A2 | \n", - "0.033333 | \n", - "NCBIGene:2206 | \n", - "biolink:coexpressed_with | \n", - "biolink:gene_associated_with_condition | \n", - "MS4A2 | \n", - "
| CEBPA | \n", - "0.033333 | \n", - "NCBIGene:1050 | \n", - "biolink:affects; biolink:regulates | \n", - "biolink:gene_associated_with_condition | \n", - "CEBPA | \n", - "
897 rows × 5 columns
\n", - "| \n", - " | score | \n", - "output_node | \n", - "predictes1 | \n", - "predictes2 | \n", - "output_node_name | \n", - "
|---|---|---|---|---|---|
| TNF | \n", - "0.400000 | \n", - "NCBIGene:7124 | \n", - "biolink:coexpressed_with; biolink:regulates; b... | \n", - "biolink:genetically_associated_with; biolink:g... | \n", - "TNF | \n", - "
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| CLDN5 | \n", - "0.033333 | \n", - "NCBIGene:7122 | \n", - "biolink:coexpressed_with | \n", - "biolink:gene_associated_with_condition | \n", - "CLDN5 | \n", - "
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\"\n", - "net.show(\"merged_graph.html\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "my-rdkit-env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/individual_endpoint_overview.ipynb b/notebooks/individual_endpoint_overview.ipynb new file mode 100644 index 0000000..134f2cf --- /dev/null +++ b/notebooks/individual_endpoint_overview.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "23613c49", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from TCT import name_resolver\n", + "from TCT import translator_metakg\n", + "from TCT import translator_kpinfo\n", + "from TCT import TCT\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "df9f0bfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'description': 'Local dev', 'url': 'http://127.0.0.1:5001'}\n", + "Skipping server without x-maturity: {'url': '/sipr'}\n" + ] + } + ], + "source": [ + "Translator_KP_info, APInames = translator_kpinfo.get_translator_kp_info()\n", + "metaKG = translator_metakg.get_KP_metadata(APInames)\n", + " \n", + "APInames, metaKG = translator_metakg.add_plover_API(APInames, metaKG)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "03e493ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ARAX Translator Reasoner - TRAPI 1.6.0',\n", + " 'Answer-coalesce(Trapi v1.5.0)',\n", + " 'Aragorn(Trapi v1.5.0)',\n", + " 'Aragorn(Trapi v1.6.0)',\n", + " 'Automat-binding-db(Trapi v1.5.0)',\n", + " 'Automat-cam-kp(Trapi v1.5.0)',\n", + " 'Automat-ctd(Trapi v1.5.0)',\n", + " 'Automat-drug-central(Trapi v1.5.0)',\n", + " 'Automat-ehr-clinical-connections-kp(Trapi v1.5.0)',\n", + " 'Automat-ehr-may-treat-kp(Trapi v1.5.0)',\n", + " 'Automat-genome-alliance(Trapi v1.5.0)',\n", + " 'Automat-gtex(Trapi v1.5.0)',\n", + " 'Automat-gtopdb(Trapi v1.5.0)',\n", + " 'Automat-gwas-catalog(Trapi v1.5.0)',\n", + " 'Automat-hetionet(Trapi v1.5.0)',\n", + " 'Automat-hgnc(Trapi v1.5.0)',\n", + " 'Automat-hmdb(Trapi v1.5.0)',\n", + " 'Automat-human-goa(Trapi v1.5.0)',\n", + " 'Automat-icees-kg(Trapi v1.5.0)',\n", + " 'Automat-intact(Trapi v1.5.0)',\n", + " 'Automat-monarchinitiative(Trapi v1.5.0)',\n", + " 'Automat-panther(Trapi v1.5.0)',\n", + " 'Automat-pharos(Trapi v1.5.0)',\n", + " 'Automat-reactome(Trapi v1.5.0)',\n", + " 'Automat-robokop(Trapi v1.5.0)',\n", + " 'Automat-string-db(Trapi v1.5.0)',\n", + " 'Automat-ubergraph(Trapi v1.5.0)',\n", + " 'Automat-viral-proteome(Trapi v1.5.0)',\n", + " 'Autonomous Relay System (ARS) TRAPI',\n", + " 'BioThings Explorer (BTE) TRAPI',\n", + " 'CATRAX BigGIM DrugResponse Performance Phase KP - TRAPI 1.5.0',\n", + " 'CATRAX Pharmacogenomics KP - TRAPI 1.5.0',\n", + " 'COHD TRAPI',\n", + " 'Clinical Trials KP - TRAPI 1.5.0',\n", + " 'Connections Hypothesis Provider API',\n", + " 'Cqs(Trapi v1.5.0)',\n", + " 'Drug Approvals KP - TRAPI 1.5.0',\n", + " 'Gene-List Network Enrichment Analysis',\n", + " 'Genetics Data Provider for NCATS Biomedical Translator Reasoners',\n", + " 'Knowledge Collaboratory API',\n", + " 'Microbiome KP - TRAPI 1.5.0',\n", + " 'MolePro',\n", + " 'Multiomics KP - TRAPI 1.5.0',\n", + " 'OpenPredict API',\n", + " 'RTX KG2 - TRAPI 1.5.0',\n", + " 'Retriever',\n", + " 'SPOKE KP for TRAPI 1.5',\n", + " 'Service Provider TRAPI',\n", + " 'Shepherd SIPR',\n", + " 'Shepherd-aragorn(Trapi v1.5.0)',\n", + " 'Shepherd-arax(Trapi v1.5.0)',\n", + " 'Shepherd-bte(Trapi v1.5.0)',\n", + " 'Sri-answer-appraiser(Trapi v1.5.0)',\n", + " 'Sri-node-normalizer(Trapi v1.5.0)',\n", + " 'Text Mined Cooccurrence API',\n", + " 'Workflow-runner(Trapi v1.5.0)',\n", + " 'imProving Agent for TRAPI 1.5',\n", + " 'mediKanren'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(Translator_KP_info['title'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76312120", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "| \n", + " | id | \n", + "title | \n", + "prod_url | \n", + "ci_url | \n", + "test_url | \n", + "
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\n", + "| \n", - " | API | \n", - "Predicate | \n", - "Subject | \n", - "Object | \n", - "URL | \n", - "
|---|---|---|---|---|---|
| 0 | \n", - "imProving Agent for TRAPI 1.5 | \n", - "biolink:has_real_world_evidence_of_association... | \n", - "biolink:ChemicalEntity | \n", - "biolink:Disease | \n", - "https://ia.transltr.io/api/v1.5/query/ | \n", - "
| 1 | \n", - "imProving Agent for TRAPI 1.5 | \n", - "biolink:entity_regulates_entity | \n", - "biolink:ChemicalEntity | \n", - "biolink:Gene | \n", - "https://ia.transltr.io/api/v1.5/query/ | \n", - "
| 2 | \n", - "imProving Agent for TRAPI 1.5 | \n", - "biolink:has_side_effect | \n", - "biolink:ChemicalEntity | \n", - "biolink:PhenotypicFeature | \n", - "https://ia.transltr.io/api/v1.5/query/ | \n", - "
| 3 | \n", - "imProving Agent for TRAPI 1.5 | \n", - "biolink:has_real_world_evidence_of_association... | \n", - "biolink:Disease | \n", - "biolink:Disease | \n", - "https://ia.transltr.io/api/v1.5/query/ | \n", - "
| 4 | \n", - "imProving Agent for TRAPI 1.5 | \n", - "biolink:has_food_component | \n", - "biolink:Food | \n", - "biolink:ChemicalEntity | \n", - "https://ia.transltr.io/api/v1.5/query/ | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 21946 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:Disease | \n", - "https://kg2cploverdb.ci.transltr.io/kg2c/query | \n", - "
| 21947 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:PhenotypicFeature | \n", - "https://kg2cploverdb.ci.transltr.io/kg2c/query | \n", - "
| 21948 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:BehavioralFeature | \n", - "https://kg2cploverdb.ci.transltr.io/kg2c/query | \n", - "
| 21949 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:ChemicalEntity | \n", - "biolink:BehavioralFeature | \n", - "https://kg2cploverdb.ci.transltr.io/kg2c/query | \n", - "
| 21950 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:MolecularMixture | \n", - "biolink:BehavioralFeature | \n", - "https://kg2cploverdb.ci.transltr.io/kg2c/query | \n", - "
21951 rows × 5 columns
\n", - "| \n", - " | API | \n", - "Predicate | \n", - "Subject | \n", - "Object | \n", - "|||||
|---|---|---|---|---|---|---|---|---|---|
| 2970 | \n", - "MolePro | \n", - "biolink:location_of | \n", - "biolink:GrossAnatomicalStructure | \n", - "biolink:MolecularMixture | \n", - "|||||
| 2971 | \n", - "MolePro | \n", - "biolink:has_side_effect | \n", - "biolink:MolecularMixture | \n", - "biolink:Behavior | \n", - "|||||
| 2972 | \n", - "MolePro | \n", - "biolink:is_active_ingredient_of | \n", - "biolink:Protein | \n", + "10640 | \n", + "Retriever | \n", + "biolink:has_substrate | \n", "biolink:SmallMolecule | \n", + "biolink:Gene | \n", "|
| 2973 | \n", - "MolePro | \n", - "biolink:affected_by | \n", - "biolink:MacromolecularComplexMixin | \n", - "biolink:ChemicalEntity | \n", - "|||||
| 2974 | \n", - "MolePro | \n", - "biolink:is_assessed_by | \n", - "biolink:ProteinComplex | \n", - "biolink:SmallMolecule | \n", + "10641 | \n", + "Retriever | \n", + "biolink:has_substrate | \n", + "biolink:Drug | \n", + "biolink:Protein | \n", "
| ... | \n", @@ -607,232 +198,76 @@ "... | \n", "||||||||
| 3793 | \n", - "MolePro | \n", + "14961 | \n", + "Retriever | \n", "biolink:affects | \n", "biolink:SmallMolecule | \n", "biolink:Gene | \n", "|||
| 3794 | \n", - "MolePro | \n", - "biolink:affects | \n", + "14962 | \n", + "Retriever | \n", + "biolink:coexists_with | \n", "biolink:Gene | \n", "biolink:SmallMolecule | \n", "||
| 3795 | \n", - "MolePro | \n", - "biolink:interacts_with | \n", + "14963 | \n", + "Retriever | \n", + "biolink:affects | \n", "biolink:Gene | \n", "biolink:SmallMolecule | \n", "||
| 3796 | \n", - "MolePro | \n", - "biolink:affected_by | \n", + "14964 | \n", + "Retriever | \n", + "biolink:interacts_with | \n", "biolink:Gene | \n", "biolink:SmallMolecule | \n", "||
| 3797 | \n", - "MolePro | \n", + "14965 | \n", + "Retriever | \n", "biolink:correlated_with | \n", "biolink:SmallMolecule | \n", "biolink:SmallMolecule | \n", "
828 rows × 4 columns
\n", - "| \n", - " | API | \n", - "Predicate | \n", - "Subject | \n", - "Object | \n", - "
|---|---|---|---|---|
| 11130 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:close_match | \n", - "biolink:Drug | \n", - "biolink:MolecularMixture | \n", - "
| 11131 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:close_match | \n", - "biolink:Drug | \n", - "biolink:ChemicalEntity | \n", - "
| 11132 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:close_match | \n", - "biolink:Drug | \n", - "biolink:SmallMolecule | \n", - "
| 11133 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:close_match | \n", - "biolink:Drug | \n", - "biolink:Drug | \n", - "
| 11134 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:close_match | \n", - "biolink:SmallMolecule | \n", - "biolink:Drug | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 21946 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:Disease | \n", - "
| 21947 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:PhenotypicFeature | \n", - "
| 21948 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:NamedThing | \n", - "biolink:BehavioralFeature | \n", - "
| 21949 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:ChemicalEntity | \n", - "biolink:BehavioralFeature | \n", - "
| 21950 | \n", - "RTX KG2 - TRAPI 1.5.0 | \n", - "biolink:applied_to_treat | \n", - "biolink:MolecularMixture | \n", - "biolink:BehavioralFeature | \n", - "
10821 rows × 4 columns
\n", + "4329 rows × 4 columns
\n", "Please upload a JSON file to view the network.
+Click on an edge (line) or node (box) in the graph above to view its detailed annotations here.
+