The Analysis of Arabidopsis thaliana Circadian Network Based on Non-stationary DBNs Approach with Flexible Time Lag Choosing Mechanism

Dynamic Bayesian networks (DBNs) are widely used in regulatory network structure inference from noisy gene expression data. However most of the previous researches assumed that the underlying stochastic processes that generates the gene expression data are stationary. Such assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to change for adapting to internal or external stimuli. In this paper we investigate a novel non-stationary DBNs method and apply the approach for the gene regulatory network inference on Arabidopsis thaliana circadian time series data. Our experimental study demonstrated that compared with recent proposed non-stationary DBNs methods, our approach has better structural prediction performance, and can potentially reduce the computational cost by improving the sampling convergence speed.