r2VIM: A new variable selection method for random forests in genome-wide association studies

被引：0
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作者：
Silke Szymczak
Emily Holzinger
Abhijit Dasgupta
James D. Malley
Anne M. Molloy
James L. Mills
Lawrence C. Brody
Dwight Stambolian
Joan E. Bailey-Wilson
机构：
[1] National Institutes of Health,Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute
[2] National Institutes of Health,Clinical Trials and Outcomes Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases
[3] National Institutes of Health,Division of Computational Bioscience, Center for Information Technology
[4] School of Medicine,Department of Clinical Medicine
[5] Trinity College Dublin,Division of Intramural Population Health Research, Eunice Shriver National Institute of Child Health and Human Development
[6] National Institutes of Health,Molecular Pathogenesis Section, Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute
[7] National Institutes of Health,Department of Ophthalmology
[8] University of Pennsylvania,Current address: Institute of Medical Informatics and Statistics
[9] University of Kiel,undefined
来源：
BioData Mining | / 9卷
关键词：
Machine learning; Random forest; Variable selection; Variable importance; Genome-wide association study; Genetic; SNP;
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