Inferring causal gene regulatory network via GreyNet: From dynamic grey association to causation.

ABSTRACT: Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks. Nevertheless, machine learning, especially deep learning, is generally a "black box," which lacks interpretability. The causality has not been well recognized in GRN inference procedures. In this article, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene-gene interac

SUBMITTER: Chen G 

PROVIDER: S-EPMC9551017 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature