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

页码：920 / 921

页数：2