Emerging deep learning methods for single-cell RNA-seq data analysis

被引：0

作者：

Jie Zheng

Ke Wang

机构：

[1] SchoolofInformationScienceandTechnology,ShanghaiTechUniversity

来源：

Quantitative Biology | 2019年 / 7卷 / 04期

关键词：

D O I：

暂无

中图分类号：

Q811.4 [生物信息论];

学科分类号：

0711 ; 0831 ;

摘要：

Deep learning is making major breakthrough in several areas of bioinformatics. Anticipating that this will occur soon for the single-cell RNA-seq data analysis, we review newly published deep learning methods that help tackle computational challenges. Autoencoders are found to be the dominant approach. However, methods based on deep generative models such as generative adversarial networks(GANs) are also emerging in this area.

引用

页码：247 / 254

页数：8

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