Abstract
Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S.Cerevisiae cell cycle gene expression data. The experimental results demonstrate that the accuracy of our method outperforms the previous work.
This work was supported in part by the National Science Foundation of China under Grant No.60321002 and the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, China.
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Zhang, Y., Deng, Z., Jiang, H., Jia, P. (2006). Gene Regulatory Network Construction Using Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM). In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_58
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DOI: https://doi.org/10.1007/11795131_58
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