A Stability-Oriented Biomarker Selection Framework Synergistically Driven by Robust Rank Aggregation and L1-Sparse Modeling.

ABSTRACT: Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods-variance thresholding, chi-square test, mutual information, ANOVA F t

SUBMITTER: Luo J 

PROVIDER: S-EPMC12735098 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature