TCT.TCT_neighborhood_finder

TCT.TCT_neighborhood_finder.neighborhood_finder(node: str | list[str], neighbor_categories: list[str], *, node_categories: list[str] | None = None, api_names: dict[str, str] | None = None, meta_kg: DataFrame | None = None, api_predicates: dict[str, list[str]] | None = None, resources: TranslatorResources | None = None, predicates_subset: list[str] | None = None, attribute_constraints: list[dict[str, Any]] | None = None, name_resolver_kwargs: dict[str, Any] | None = None, node_normalizer_kwargs: dict[str, Any] | None = None) → FinderResult

Find one-hop neighbors for one or more biomedical concepts.

Parameters:

nodestr or list[str]

Source node or nodes. Each value may be a CURIE or human-readable string. Human-readable strings are resolved with Name Resolver and then normalized with Node Normalizer.

neighbor_categorieslist[str]

Desired neighbor categories. Values may be short names like "Drug" or full Biolink names like "biolink:Drug".

node_categorieslist[str], optional

Category override for source nodes. If omitted, categories are inferred from the first normalized source node.

resourcesTranslatorResources, optional

Preloaded Translator resources. If omitted, the module-level singleton is loaded on first use and reused.

api_names, meta_kg, api_predicatesoptional

Advanced partial overrides for the Translator resources used by the neighborhood implementation.

predicates_subsetlist[str], optional

Optional predicate filter applied after MetaKG predicate selection.

attribute_constraintslist[dict], optional

TRAPI attribute constraints passed through to query construction.

name_resolver_kwargsdict, optional

Extra keyword arguments for name_resolver.lookup.

node_normalizer_kwargsdict, optional

Extra keyword arguments for node_normalizer.get_normalized_nodes.

Returns:

FinderResult

Convenience wrapper containing resolved input nodes, parsed neighborhood knowledge graph, results, auxiliary graphs, and raw TRAPI-style output.

Examples

>>> from TCT import neighborhood_finder
>>> result = neighborhood_finder("asthma", ["SmallMolecule", "Drug"])
>>> result.knowledge_graph["nodes"]
{...}

TCT.TCT_neighborhood_finder.parse_results_for_neighborhood_finder(start_node_id: str, results: dict, start_node_categories: list | None = None, end_node_categories: list | None = None, get_node_info: bool = True, scoring_method: str = 'infores') → dict

Converts the results of two TRAPI queries into the same general json format as the other pathfinder APIs.

Returns:

A dict of the format {‘query_graph’: …, ‘knowledge_graph’: …, ‘results’:, ‘auxiliary_graphs’:…}

TCT.TCT_neighborhood_finder.parse_results_for_neighborhood_finder_multiple_inputs(start_node_ids: list[str], results: dict, start_node_categories: list | None = None, end_node_categories: list | None = None, get_node_info: bool = True, scoring_method: str = 'infores') → dict

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’.