An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings

BACKGROUND: As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disea...

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Detalles Bibliográficos
Autores principales: Goldstein, Benjamin A, Hubbard, Alan E, Cutler, Adele, Barcellos, Lisa F
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Research article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896336/
https://www.ncbi.nlm.nih.gov/pubmed/20546594
http://dx.doi.org/10.1186/1471-2156-11-49

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896336/
https://www.ncbi.nlm.nih.gov/pubmed/20546594
http://dx.doi.org/10.1186/1471-2156-11-49

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