An integrative multiomics random forest framework for robust biomarker discovery.

ABSTRACT:

Background

High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating multiple omics layers measured on the same samples can reveal cross-layer molecular hubs that single-layer analyses miss. However, many existing integrative methods rely on linear assumptions or univariate feature importance, limiting their ability to capture nonlinear and interaction-driven dependencies across data modalities.

Results

We present an unsupervised, multivariate random forest (MRF) framework with an inverse minimal depth (IMD) importance to prioritize shared biomarkers across omics. In each forest, one layer serves as a multivariate response and the other as predictors; IMD summarizes how

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12821379 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature