Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise...

Descripción completa

Detalles Bibliográficos
Autores principales: Mao, Guo, Pang, Zhengbin, Zuo, Ke, Wang, Qinglin, Pei, Xiangdong, Chen, Xinhai, Liu, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Problem Solving Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661972/
https://www.ncbi.nlm.nih.gov/pubmed/37985457
http://dx.doi.org/10.1093/bib/bbad414

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661972/
https://www.ncbi.nlm.nih.gov/pubmed/37985457
http://dx.doi.org/10.1093/bib/bbad414

Ejemplares similares