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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Veröffentlicht in:	Journal of computational biology Jg. 30; H. 5; S. 619
Hauptverfasser:	Mao, Guo, Pang, Zhengbin, Zuo, Ke, Liu, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.05.2023
Schlagworte:	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
Online-Zugang:	Weitere Angaben
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