An Integrative Multi-Omics Random Forest Framework for Robust Biomarker Discovery.

ABSTRACT: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)-based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of m

SUBMITTER: Zhang W 

PROVIDER: S-EPMC11908250 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature