Usage

API for assembling biomedial lexica.

class Configuration(**data: Any)[source]

A configuration for construction of a lexicon.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Input(*, processor: Literal['pyobo', 'bioontologies', 'ssslm', 'gilda'], source: str, ancestors: None | str | list[str] = None, kwargs: dict[str, Any] | None = None)[source]

An input towards lexicon assembly.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Processor

A processor available as a literal mapping input

alias of Literal[‘pyobo’, ‘bioontologies’, ‘ssslm’, ‘gilda’]

assemble_grounder(configuration: Configuration, mappings: list[semra.Mapping] | None = None, *, extra_terms: list[LiteralMapping] | None = None, include_biosynonyms: bool = True) ssslm.Grounder[source]

Assemble terms from multiple resources and load into a grounder.

assemble_terms(configuration: Configuration, mappings: list[semra.Mapping] | None = None, *, extra_terms: list[LiteralMapping] | None = None, include_biosynonyms: bool = True, raw_path: Path | None = None, processed_path: Path | None = None, gilda_path: Path | None = None, summary_path: Path | None = None) list[LiteralMapping][source]

Assemble terms from multiple resources.

get_literal_mappings(prefix: str, *, ancestors: None | str | Sequence[str] = None, processor: Literal['pyobo', 'bioontologies', 'ssslm', 'gilda'], **kwargs: Any) list[LiteralMapping][source]

Iterate over all terms from a given prefix.

load_grounder(grounder: ssslm.GrounderHint) ssslm.Grounder[source]

Load a grounder, potentially from a remote location.

summarize_terms(literal_mappings: list[LiteralMapping]) BaseModel[source]

Summarize terms.