A novel statistical feature selection framework for biomarker discovery and cancer classification via multiomics integration.

ABSTRACT:

Background

Early cancer diagnosis is essential for improving prognosis and guiding treatment. However, the high dimensionality and complexity of omics data present major challenges. Computational approaches that extract stable biomarkers and enable reliable classification across cancer types and stages are needed.

Methods

A novel feature selection method, sDCFE (synergistic Discriminative Cluster-based Feature Extraction), was developed by extending Fisher-like variance analysis with a median absolute deviation (MAD) regularization term and a cluster separation component to enhance robustness and interpretability. Features selected by sDCFE were compared with those obtained from XGBoost, and the intersected set of 82 genes was evaluated through functional enrichment (KEGG, R

SUBMITTER: Ghaleb MS 

PROVIDER: S-EPMC12822226 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature