Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data

In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to...

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Vydané v:	Journal of computational biology Ročník 30; číslo 5; s. 619
Hlavní autori:	Mao, Guo, Pang, Zhengbin, Zuo, Ke, Liu, Jie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.05.2023
Predmet:	Gene Expression Profiling - methods Gene Regulatory Networks Neural Networks, Computer Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-Cell Gene Expression Analysis the atrous spatial pyramid pooling (ASPP) module and the squeeze-and-excitation (SE) module scRNA-seq data 2D co-expression matrix convolutional neural network
ISSN:	1557-8666, 1557-8666
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