Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.

ABSTRACT:

Motivation

Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that

SUBMITTER: Wang Y 

PROVIDER: S-EPMC3853073 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature