Machine learning technology in the application of genome analysis: A systematic review.


Journal

Gene
ISSN: 1879-0038
Titre abrégé: Gene
Pays: Netherlands
ID NLM: 7706761

Informations de publication

Date de publication:
15 Jul 2019
Historique:
received: 24 01 2019
revised: 17 04 2019
accepted: 22 04 2019
pubmed: 27 4 2019
medline: 14 6 2019
entrez: 27 4 2019
Statut: ppublish

Résumé

Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever increasing biological data. In this review, we summarized major ML algorithms and conditions that must be paid attention to when applying these algorithms to genomic problems in details and we provided a list of examples from different perspectives and data analysis challenges at present.

Identifiants

pubmed: 31026571
pii: S0378-1119(19)30420-2
doi: 10.1016/j.gene.2019.04.062
pii:
doi:

Types de publication

Journal Article Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

149-156

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Jie Wu (J)

Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Biological Sciences, China Agricultural University, Beijing, PR China; State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, PR China.

Yiqiang Zhao (Y)

Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Biological Sciences, China Agricultural University, Beijing, PR China; State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, PR China. Electronic address: yiqiangz@cau.edu.cn.

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Classifications MeSH