PUBLICATION DATE

2015-05-07

AUTHORS

William Stafford Noble, Maxwell W. Libbrecht

TITLE

Machine learning applications in genetics and genomics

ISSUE

6

VOLUME

16

ISSN (print)

1471-0056

ISSN (electronic)

1471-0064

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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37 TRIPLES      29 PREDICATES      37 URIs      19 LITERALS

Subject Predicate Object
1 articles:378b8cb548d6f674ad423357c1da8069 sg: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.
2 sg:coverYear 2015
3 sg:coverYearMonth 2015-06
4 sg:ddsIdJournalBrand 41576
5 sg:doi 10.1038/nrg3920
6 sg:doiLink http://dx.doi.org/10.1038/nrg3920
7 sg:hasArticleType article-types:reviews
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11 sg:hasFieldOfResearchCode anzsrc-for:01
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16 journals:3a7260554b60b54b82ab939fb0784606
17 sg:hasJournalBrand journal-brands:921803ac6c5c16bea8b5e7cb481348b8
18 sg:hasSubject subjects:genomics
19 subjects:machine-learning
20 subjects:statistical-methods
21 sg:indexingDatabase Scopus
22 Web of Science
23 sg:issnElectronic 1471-0064
24 sg:issnPrint 1471-0056
25 sg:issue 6
26 sg:license http://scigraph.springernature.com/explorer/license/
27 sg:npgId nrg3920
28 sg:pageEnd 332
29 sg:pageStart 321
30 sg:publicationDate 2015-05-07
31 sg:publicationYear 2015
32 sg:publicationYearMonth 2015-05
33 sg:scigraphId 378b8cb548d6f674ad423357c1da8069
34 sg:title Machine learning applications in genetics and genomics
35 sg:volume 16
36 rdf:type sg:Article
37 rdfs:label Article: Machine learning applications in genetics and genomics
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