Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs.

ABSTRACT:

Motivation

Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference.

Results

Based on the Gaussian graphical model with adaptive gene importance regularization, we dev

SUBMITTER: Leng J 

PROVIDER: S-EPMC8756181 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature