A primer on deep learning in genomics

被引：444

作者：

Zou, James ^{[1
,2
,3
]}

Huss, Mikael ^{[4
,5
]}

Abid, Abubakar ^{[3
]}

Mohammadi, Pejman ^{[6
,7
]}

Torkamani, Ali ^{[6
,7
]}

Telenti, Amalio ^{[6
,7
]}

机构：

[1] Stanford Univ, Dept Biomed Data Sci, Palo Alto, CA 94304 USA

[2] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA

[3] Stanford Univ, Dept Elect Engn, Palo Alto, CA 94304 USA

[4] Peltarion, Stockholm, Sweden

[5] Karolinska Inst, Dept Learning Informat Management & Eth, Stockholm, Sweden

[6] Scripps Res Translat Inst, La Jolla, CA 92037 USA

[7] Scripps Res Inst, Dept Integrat Struct & Computat Biol, La Jolla, CA 92037 USA

来源：

NATURE GENETICS | 2019年 / 51卷 / 01期

基金：

美国国家科学基金会;

关键词：

DNA;

D O I：

10.1038/s41588-018-0295-5

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

引用

页码：12 / 18

页数：7

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