title: Random forests for genomic data analysis.
abstract: Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly data adaptive, applies to "large p, small n" problems, and is able to account for correlation as well as interactions among features. This makes RF particularly appealing for high-dimensional genomic data analysis. In this article, we systematically review the applications and recent progresses of RF for genomic data, including prediction and classification, variable selection, pathway analysis, genetic association and epistasis detection, and unsupervised learning.
authors.affiliation: Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA. steven.chen@vanderbilt.edu
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.forename: Hemant
authors.initials: H
authors.lastname: Ishwaran
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D004843:Epistasis, Genetic; D056726:Genetic Association Studies; D023281:Genomics / Q000379:methods*