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Nat Rev Genet. 2015 Jun;16(6):321-32. doi: 10.1038/nrg3920. Epub 2015 May 7.

Machine learning applications in genetics and genomics.

Author information

1
Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA.
2
1] Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA. [2] Department of Genome Sciences, University of Washington, 3720 15th Ave NE Seattle, Washington 98195-5065, USA.

Abstract

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

PMID:
25948244
PMCID:
PMC5204302
DOI:
10.1038/nrg3920
[Indexed for MEDLINE]
Free PMC Article

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