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›› 2013, Vol. 30 ›› Issue (6): 806-812.DOI: 10.7523/j.issn.2095-6134.2013.06.014

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Modelling non-stationary gene regulatory networks by combining microarrys with biological knowledge

NI Xiao-Hong, SUN Ying-Fei   

  1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2012-09-12 Revised:2013-03-26 Online:2013-11-15

Abstract:

In order to construct gene regulatory network, we propose a non-stationary dynamic Bayesian networks method that systematically integrates expression data with multiple sources of prior knowledge. Our method is based on Gaussian mixture Bayesian network model, change point process, and separate energy function of prior knowledge. Using an reversible jump Markov chain Monte Carlo sampling algorithm, we divide data into disjunct compartments, infer network structures, and measure the influence of the respective prior knowledge. Finally, we apply our approach to treat both synthetic data and biological data. The results show that the proposed method improves the network reconstruction accuracy.

Key words: Bayesian networks, gene regulatory networks, Markov chain Monte Carlo, change point process

CLC Number: