Random Forests for Genetic Association Studies

Statistical Applications in Genetics and Molecular Biology - Tập 10 Số 1 - 2011
Benjamin A. Goldstein1, Eric C. Polley2,3, Farren Briggs4
1Quantitative Sciences Unit, Department of Medicine, Stanford University, USA;
2NATIONAL INSTITUTES OF HEALTH
3Quantitative Sciences Unit, Department of Medicine, Stanford University. Eric C. Polley, Biometric Research Branch, National Cancer Institute,
4University of California at Berkeley

Tóm tắt

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Từ khóa


Tài liệu tham khảo

Thông tin
Thông tin xuất bản

Statistical Applications in Genetics and Molecular Biology

Tập 10 Số 1

Thông tin tác giả