Reverse engineering gene networks using global-local shrinkage rules.

ABSTRACT: Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as tho

SUBMITTER: Panchal V 

PROVIDER: S-EPMC6936010 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature