anc_model: ANC model for literature-based discovery with biomedical term...

In LBDiscover: Literature-Based Discovery Tools for Biomedical Research

ANC model for literature-based discovery with biomedical term filtering

Description

This function implements an improved ANC model that ensures only biomedical terms are used as intermediaries.

Usage

anc_model(
  co_matrix,
  a_term,
  n_b_terms = 3,
  c_type = NULL,
  min_score = 0.1,
  n_results = 100,
  enforce_biomedical_terms = TRUE,
  b_term_types = c("protein", "gene", "chemical", "pathway", "drug", "disease",
    "biological_process"),
  validation_function = is_valid_biomedical_entity
)

Arguments

co_matrix	A co-occurrence matrix produced by create_cooccurrence_matrix().
a_term	Character string, the source term (A).
n_b_terms	Number of intermediate B terms to consider.
c_type	Character string, the entity type for C terms. If NULL, all types are considered.
min_score	Minimum score threshold for results.
n_results	Maximum number of results to return.
enforce_biomedical_terms	Logical. If TRUE, enforces strict biomedical term filtering.
b_term_types	Character vector of entity types allowed for B terms.
validation_function	Function to validate biomedical terms.

Value

A data frame with ranked discovery results.

LBDiscover documentation built on June 16, 2025, 5:09 p.m.