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

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput technologies generate diverse omics data (genomics, transcriptomics, epigenomics, proteomics).
• Integrating multi-omics data is crucial for understanding complex traits and diseases.
• Identifying shared biomarkers across data layers presents a significant challenge.

Purpose of the Study:

• To develop an advanced framework for integrative variable selection from multi-omics data.
• To enhance biomarker discovery by efficiently identifying key shared features across diverse data types.
• To improve the biological and clinical interpretation of complex molecular data.

Main Methods:

• A multivariate random forest (MRF)-based framework was developed.
• A novel inverse minimal depth (IMD) metric was employed for predictor ranking.
• The method assigns response variables to tree nodes for enhanced feature selection.
• Simulations and analyses of The Cancer Genome Atlas (TCGA) multi-omics datasets were performed.

Main Results:

• The MRF-MRF-based framework with IMD demonstrated superior performance over existing integrative techniques.
• The method successfully identified biologically meaningful biomarkers and pathways.
• Selected biomarkers showed correlation with known biological networks and patient stratification capabilities.
• The approach effectively handles high-dimensionality and noise in multi-omics data.

Conclusions:

• The developed MRF-based framework offers a robust and scalable solution for multi-omics biomarker discovery.
• The method facilitates the identification of clinically relevant biomarkers, aiding in patient stratification.
• This approach advances integrative multi-omics analysis, accelerating biological and clinical insights.