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

BACKGROUND: Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to...

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Dettagli Bibliografici
Pubblicato in:BioData Min
Autori principali: Szymczak, Silke, Holzinger, Emily, Dasgupta, Abhijit, Malley, James D., Molloy, Anne M., Mills, James L., Brody, Lawrence C., Stambolian, Dwight, Bailey-Wilson, Joan E.
Natura: Artigo
Lingua:Inglês
Pubblicazione: BioMed Central 2016
Soggetti:
Methodology
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC4736152/
https://ncbi.nlm.nih.gov/pubmed/26839594
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13040-016-0087-3
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