An integrative multiomics random forest framework for robust biomarker discovery
MRF-IMD provides a scalable and interpretable framework for multiomics integration that reliably identifies cross-layer biomarkers when nonlinear and interaction-driven dependencies are present. This approach advances robust biomarker discovery beyond the limits of linear integrative methods.
Authors
- Xi Chen (ORCID: https://orcid.org/0000-0002-9706-3571)
- Brian D. Lehmann (ORCID: https://orcid.org/0000-0003-0407-5248)
- Weidong Zhang (ORCID: https://orcid.org/0000-0002-7384-2490)
- Lily Wang (ORCID: https://orcid.org/0000-0002-6630-8767)
- Hanchen Huang
Institutions
- University of Miami (US)
- Dr. John T. Macdonald Foundation (US)
- Sylvester Comprehensive Cancer Center (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- GigaScience
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1093/gigascience/giaf148
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Sylvester Comprehensive Cancer Center, University of Miami Health Systems
- National Cancer Institute