A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

BACKGROUND: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle...

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Detalles Bibliográficos
Autores principales: Sauta, Elisabetta, Demartini, Andrea, Vitali, Francesca, Riva, Alberto, Bellazzi, Riccardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7257163/
https://www.ncbi.nlm.nih.gov/pubmed/32471360
http://dx.doi.org/10.1186/s12859-020-3510-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7257163/
https://www.ncbi.nlm.nih.gov/pubmed/32471360
http://dx.doi.org/10.1186/s12859-020-3510-1

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