Weighted Fused Pathway Graphical Lasso for Joint Estimation of Multiple Gene Networks.

ABSTRACT: Gene regulatory networks (GRNs) are often inferred based on Gaussian graphical models that could identify the conditional dependence among genes by estimating the corresponding precision matrix. Classical Gaussian graphical models are usually designed for single network estimation and ignore existing knowledge such as pathway information. Therefore, they can neither make use of the common information shared by multiple networks, nor can they utilize useful prior information to guide the estimation. In this paper, we propose a new weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple networks by incorporating prior knowledge derived from known pathways and gene interactions. Based on the assumption that two genes are less likely to be connected if they do not participa

SUBMITTER: Wu N 

PROVIDER: S-EPMC6662592 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature