Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most availab...

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Detalles Bibliográficos
Autores principales: Sun, Xiaoqiang, Zhang, Ji, Nie, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968745/
https://www.ncbi.nlm.nih.gov/pubmed/33667222
http://dx.doi.org/10.1371/journal.pcbi.1008379

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968745/
https://www.ncbi.nlm.nih.gov/pubmed/33667222
http://dx.doi.org/10.1371/journal.pcbi.1008379

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