期刊
GENOMICS
卷 99, 期 6, 页码 323-329出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2012.04.003
关键词
Random forests; Random survival forests; Classification; Prediction; Variable selection; Genomic data analysis
资金
- 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
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据