ANC model for literature-based discovery with biomedical term filtering

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.