abc_model_sig: Apply the ABC model with statistical significance testing

In LBDiscover: Literature-Based Discovery Tools for Biomedical Research

Apply the ABC model with statistical significance testing

Description

This function extends the ABC model with statistical significance testing to evaluate the strength of discovered connections.

Usage

abc_model_sig(
  co_matrix,
  a_term,
  c_term = NULL,
  a_type = NULL,
  c_type = NULL,
  min_score = 0.1,
  n_results = 100,
  n_permutations = 1000,
  scoring_method = c("multiplication", "average", "combined", "jaccard")
)

Arguments

co_matrix	A co-occurrence matrix produced by create_cooccurrence_matrix().
a_term	Character string, the source term (A).
c_term	Character string, the target term (C). If NULL, all potential C terms will be evaluated.
a_type	Character string, the entity type for A terms. If NULL, all types are considered.
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.
n_permutations	Number of permutations for significance testing.
scoring_method	Method to use for scoring ABC connections.

Value

A data frame with ranked discovery results and p-values.

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