Abstract: In systems biology,the hidden Markov model non-homogeneous dynamic Bayesian network(HMM-DBN) can reasonably infer the regulatory relationships in periodic gene expression data and is one of the important methods to reconstruct gene regulatory networks.But it usually assumes complete independence of its regulatory parameters(the parameters of each time periods need to be inferred independently),and the parameter assumption(complete independence) is equivalent to ignore the continuity of biological evolutionary processes in nature,which affects the accuracy of network reconstruction.Aiming at the above problems and combining multiple changepoint processes,a hidden Markov model non-homogeneous dynamic Bayesian network with global coupling of parameters(GCHMM-DBN) is proposed.The GCHMM-DBN model achieves the global coupling of regression parameters by adding the global coupling hyperparameters,sharing the noise variance hyperparameters and signal-to-noise ratio hyperparameters of all time periods in the similarity Gaussian distribution based on the HMM-DBN,and finally improving the reconstruction accuracy of the gene regulation network.Experimental results on Saccharomyces cerevisiae(yeast) and synthetic RAF datasets show that the GCHMM-DBN model has higher accuracy of gene regulatory network reconstruction compared with the classical HMM-DBN model.

Key words: Dynamic Bayesian networks, Gene regulatory networks, Global coupling, Non-homogeneous, Markov chain Monte Carlo