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 to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information.

Results

In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia t

SUBMITTER: Sauta E 

PROVIDER: S-EPMC7257163 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature