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Genomics. 2012 Jun;99(6):323-9. doi: 10.1016/j.ygeno.2012.04.003. Epub 2012 Apr 21.

Random forests for genomic data analysis.

Author information

1
Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA. steven.chen@vanderbilt.edu

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.

PMID:
22546560
PMCID:
PMC3387489
DOI:
10.1016/j.ygeno.2012.04.003
[Indexed for MEDLINE]
Free PMC Article

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