Journal
GENOMICS
Volume 99, Issue 6, Pages 323-329Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2012.04.003
Keywords
Random forests; Random survival forests; Classification; Prediction; Variable selection; Genomic data analysis
Funding
- National Cancer Institute [5P30CA068485-15]
- DMS from the National Science Foundation [1148991]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1148991] Funding Source: National Science Foundation
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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. (C) 2012 Elsevier Inc. All rights reserved.
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