Inference of differential gene regulatory networks based on gene expression and genetic perturbation data.

ABSTRACT:

Motivation

Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy.

Results

In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data,

SUBMITTER: Zhou X 

PROVIDER: S-EPMC6956787 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature