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.

journal_name

Genomics

journal_title

Genomics

authors

Chen X,Ishwaran H

doi

10.1016/j.ygeno.2012.04.003

subject

Has Abstract

pub_date

2012-06-01 00:00:00

pages

323-9

issue

6

eissn

0888-7543

issn

1089-8646

pii

S0888-7543(12)00062-6

journal_volume

99

pub_type

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