Inferring causal gene regulatory network via GreyNet: From dynamic grey association to causation - Chen, Guangyi; Liu, Zhi-Ping (Frontiers in Bioengineering and Biotechnology, 2022)
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 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 is lacking interpretability. And the causality has not been well recognized in GRN inference procedures. In this paper, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene-gene interactions in the uncertain regulatory system. Then, we incorporate generalized multivariate Granger causality regression methods to transform the dynamic grey association into causation to generate directional regulatory links. We evaluate our model on the DREAM4 in-silico benchmark dataset and real-world hepatocellular carcinoma (HCC) time-series data. We achieve competitive results on the DREAM4 compared with other state-of-the-art algorithms and gain meaningful GRN structure on HCC data respectively.