r2VIM: A new variable selection method for random forests in genome-wide association studies-Reference-Cited by-同舟云学术

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

Published:2016-02-01 Issue:1 Volume:9 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Szymczak Silke,Holzinger Emily,Dasgupta Abhijit,Malley James D.,Molloy Anne M.,Mills James L.,Brody Lawrence C.,Stambolian Dwight,Bailey-Wilson Joan E.

Funder

National Eye Institute

National Institute of Child Health and Human Development

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Genetics,Molecular Biology,Biochemistry

Link

http://link.springer.com/content/pdf/10.1186/s13040-016-0087-3

Reference40 articles.

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2. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–53.

3. Breiman L. Random Forests. Mach Learn. 2001;45:5–32.

4. Goldstein BA, Polley EC, Briggs FBS. Random Forests for genetic association studies. Stat Appl Genet Mol. 2011;10:32.

5. Jiang R, Tang W, Wu X, Fu W. A random forest approach to the detection of epistatic interactions in case–control studies. BMC Bioinformatics. 2009;10:S65.

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