Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

BACKGROUND: Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. RESULTS: Thi...

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Detalles Bibliográficos
Autores principales: Shi, Ming, Tan, Sheng, Xie, Xin-Ping, Li, Ao, Yang, Wulin, Zhu, Tao, Wang, Hong-Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559338/
https://www.ncbi.nlm.nih.gov/pubmed/33054712
http://dx.doi.org/10.1186/s12864-020-07079-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7559338/
https://www.ncbi.nlm.nih.gov/pubmed/33054712
http://dx.doi.org/10.1186/s12864-020-07079-8

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