MADVAR: a lightweight, data-driven tool for automated feature selection in omics data.

ABSTRACT:

Motivation

High-throughput biological data provides rich opportunities for discovery, but its vastness leads to the inclusion of many irrelevant features that hinder effective analysis, especially in unsupervised clustering and machine learning tasks. Traditional feature selection methods such as correlation filtering, PCA, mutual information, and Laplacian scores often either eliminate important features or demand extensive computational resources, and their thresholds are usually arbitrary rather than data-driven.

Results

MADVAR addresses these challenges as a lightweight R package for automated feature selection in omics data, introducing two data-driven methods-madvar and intersectDistributions-that define thresholds based on the statistical structure of the data itself.

SUBMITTER: Silberberg G 

PROVIDER: S-EPMC12449246 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature