A software framework for end-to-end genomic sequence analysis with deep learning

被引：0
|
作者：
Klie, Adam [1 ]
Carter, Hannah [1 ]
机构：
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
来源：
NATURE COMPUTATIONAL SCIENCE | 2023年 / 3卷 / 11期
关键词：
Compendex;
D O I：
10.1038/s43588-023-00557-5
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Using deep learning methods to study gene regulation has become popular, but designing accessible and customizable software for this purpose remains a challenge. This work introduces a computational toolkit called EUGENe that facilitates the development of end-to-end deep learning workflows in regulatory genomics.
引用
收藏
页码：920 / 921
页数：2
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