Abstract
As a kind of high-precision correlation measurement method, Part Mutual Information (PMI) was firstly introduced into Bayesian Networks (BNs) structure learning algorithm in the paper. Compared to the general search scoring algorithm which set the initial network as an empty network without edge, our training algorithm initialized the network structure as an undirected network. That meant that our initial network identified the genes related to each other. And then the following algorithm only needed to determine the direction of the edges in the network. In the paper, we quoted the classic K2 algorithm based on Bayesian Dirichlet Equivalence (BDE) scoring function to search the direction of the edges. To test the proposed method, We carried out our experiment on two networks: the simulated gene regulatory network and the SOS DNA Repair network of Ecoli bacterium. And via comparison of different methods for SOS DNA Repair network, our proposed method was proved to be effective.
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Acknowledgment
This work was supported by the National Key Research and Development Program of China (2016YFC106000), the National Natural Science Foundation of China (Grant No. 61671220, 61640218, 61201428), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).
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Meng, Q., Chen, Y., Wang, D., Meng, Q. (2017). Learning Bayesian Networks Structure Based Part Mutual Information for Reconstructing Gene Regulatory Networks. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_57
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DOI: https://doi.org/10.1007/978-3-319-63312-1_57
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