Random forests for genomic data analysis

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.

► Applications and recent progresses of random forests for genomic data analysis.
► Methods for variable selection by random forests and random survival forests.
► Classification and prediction of random forests using high-dimensional genomic data.
► Genetic association and epistasis detection using random forests on GWA data.