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Machine learning applications in genetics and genomics

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Libbrecht, Maxwell W., Noble, William Stafford·2015·Nature Reviews Genetics
DOI 10.1038/nrg3920
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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.

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Metadata

Authors
Libbrecht, M. W., & Noble, W. S.
Venue
Nature Reviews Genetics
Publisher
Springer Science and Business Media LLC
Published
2015-05-07
Volume
16
Issue
6
Pages
321-332
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
DOI
10.1038/nrg3920
OpenAlex
W1505191356
Cited by
2,084
References
9
FWCI
6.77
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics. https://doi.org/10.1038/nrg3920

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