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Article

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

BMC genetics - 14 Jun 2010

Goldstein Benjamin A, Hubbard Alan E, Cutler Adele, Barcellos Lisa F

Abstract excerpt

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 disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One...

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Identifiers

PMID: 20546594
DOI: 10.1186/1471-2156-11-49
PMCID: PMC2896336

Genes

CTNNA3
MPHOSPH9
PHACTR2

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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