Automated sequence-based annotation and interpretation of the human genome

被引：1
|
作者：
Kundaje, Anshul [1 ,2 ]
Meuleman, Wouter [3 ,4 ]
机构：
[1] Stanford Univ, Dept Genet, Palo Alto, CA 94304 USA
[2] Stanford Univ, Dept Comp Sci, Palo Alto, CA 94304 USA
[3] Altins Inst Biomed Sci, Seattle, WA 98121 USA
[4] Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA
来源：
NATURE GENETICS | 2022年 / 54卷 / 07期
关键词：
D O I：
10.1038/s41588-022-01123-x
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
A machine-learning model produces summarized sequence representations of genomic regulatory activity, and provides a functional view of regulatory DNA variation in the human genome, with the aim of better understanding the role of sequence variation in health and disease.
引用
收藏
页码：916 / 917
页数：2
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