Source code for indra.sources.eidos.bio_processor

from typing import Any, Callable, Mapping, Optional

from indra.statements import Agent
from indra.statements import Activation, Inhibition
from indra.ontology.standardize import standardize_agent_name
from .processor import EidosProcessor

GrounderResult = Mapping[str, str]
Grounder = Callable[[str, Optional[str]], GrounderResult]

[docs]class EidosBioProcessor(EidosProcessor): """Class to extract biology-oriented INDRA statements from Eidos output in a way that agents are grounded to biomedical ontologies.""" def __init__(self, json_dict, grounder: Optional[Grounder] = None): super().__init__(json_dict) if grounder: self.grounder = grounder else: self.grounder = default_grounder_wrapper def get_regulate_activity(self, stmt): context = stmt.evidence[0].text subj = self.get_agent_bio(stmt.subj.concept, context=context) obj = self.get_agent_bio(stmt.obj.concept, context=context) if not subj or not obj: return None pol = stmt.overall_polarity() stmt_type = Activation if pol == 1 or not pol else Inhibition bio_stmt = stmt_type(subj, obj, evidence=stmt.evidence) return bio_stmt def extract_statements(self): self.extract_causal_relations() bio_stmts = [] for stmt in self.statements: bio_stmt = self.get_regulate_activity(stmt) if bio_stmt: bio_stmts.append(bio_stmt) self.statements = bio_stmts def get_agent_bio(self, concept, context=None): return get_agent_bio(concept, context=context, grounder=self.grounder)
def get_agent_bio(concept, context=None, grounder: Optional[Grounder] = None): if not grounder: grounder = default_grounder_wrapper # Note that currently is the canonicalized entity text # whereas db_refs['TEXT'] is the unaltered original entity text raw_txt = concept.db_refs['TEXT'] norm_txt = # We ground first the raw entity text and if that cannot be grounded, # the normalized entity text. The agent name is chosen based on the # first text that was successfully grounded, or if no grounding was # obtained, is chosen as the normalized text for txt in (raw_txt, norm_txt): gr = grounder(txt, context=context) if gr: name = txt break else: gr = {} name = norm_txt # We take whatever grounding and name are available and then # standardize the agent. agent = Agent(name, db_refs={'TEXT_NORM': norm_txt, 'TEXT': raw_txt, **gr}) standardize_agent_name(agent, standardize_refs=True) return agent def default_grounder_wrapper(text: str, context: Optional[str]) -> GrounderResult: # Import here to avoid this when working in INDRA World context from indra.preassembler.grounding_mapper.gilda import get_grounding grounding, _ = get_grounding(text, context=context, mode='local') return grounding