期刊
GENETIC EPIDEMIOLOGY
卷 35, 期 -, 页码 S5-S11出版社
WILEY
DOI: 10.1002/gepi.20642
关键词
unsupervised learning; supervised learning; cluster analysis; logistic regression; Poisson regression; logic regression; LASSO; ridge regression; decision trees; random forests; cross-validation; software
资金
- National Institute for Arthritis and Musculoskeletal and Skin Diseases
- National Human Genome Research Institute
- Center for Information Technology of the National Institutes of Health
- National Institutes of Health, National Heart, Lung, and Blood Institute [HL100245]
- CENTER FOR INFORMATION TECHNOLOGY [ZIACT000268, ZIACT000271] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [RC1HL100245] Funding Source: NIH RePORTER
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [ZIAHG000153] Funding Source: NIH RePORTER
Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment, and a description of available software resources for these methods. Genet. Epidemiol. 35:S5S11, 2011. (C) 2011 Wiley Periodicals, Inc.
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