Review
doi: 10.1016/j.ygeno.2012.04.003. Epub 2012 Apr 21.Random forests for genomic data analysis
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- PMID:22546560
- PMCID: PMC3387489
- DOI: 10.1016/j.ygeno.2012.04.003
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Review
Random forests for genomic data analysis
Xi Chen et al. Genomics.2012 Jun.
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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.
Copyright © 2012 Elsevier Inc. All rights reserved.
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