Iterative random forests to discover predictive and stable high-order interactions
Abstract
We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 19, 2018
- Source ID
- 10.1073/pnas.1711236115
Entities
People
- Bin Yu
- James B. Brown
- Karl Kumbier
- Sumanta Basu
Organizations
- Army Research Office
- Cornell University
- National Human Genome Research Institute
- National Science Foundation
- Office of Naval Research
- United States Department of Energy
- United States National Library of Medicine
- University of Birmingham