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

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

Abstract 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 t...

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Bibliographic Details
Main Authors: Elisabetta Sauta, Andrea Demartini, Francesca Vitali, Alberto Riva, Riccardo Bellazzi
Format: Article
Language:English
Published: BMC 2020-05-01
Series:BMC Bioinformatics
Subjects:
Genomic transcriptional networks
omics-data fusion
Bayesian networks
Hybrid structure learning algorithm
Online Access:http://link.springer.com/article/10.1186/s12859-020-3510-1

Internet

http://link.springer.com/article/10.1186/s12859-020-3510-1

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