Abstract
One great challenge in biological study is to identify the complex gene regulation networks. For decades, scientists have developed different methods to construct the gene networks, which represent a series of gene regulations. Bayesian network is a flexible method to reconstruct the network structure with relevant conditional probabilities. However, finding a proper network structure using Bayesian network is a tough problem. In contrast, Boolean network provides low computational complexity but lose some information when discretizing the data into binary form. In this study, we propose a new method to integrate the approaches of Boolean and Bayesian networks, which will be termed as the Boolean-Bayesian network, to reduce the computation cost and to preserve the inference as in Bayesian network. Boolean-Bayesian network first suggests a set of pairwise relations from s-p-score associated with networks (SPAN) method and identifies the proper network by maximizing a posteriori probability estimate. Then, the relevant conditional probabilities can be computed for each relation in the network. Hence, the reconstructed network is determined. Different simulations have been conducted based on the proposed method, and an empirical study is applied to validate the method. This study demonstrates that Boolean-Bayesian network can speed up the process in finding the network structure and provide statistical inference as in Bayesian network.
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Acknowledgements
This study is primarily supported by the Ministry of Science and Technology, Taiwan, ROC (grant numbers MOST 110-2118-M-A49-002-MY3 and MOST 110-2634-F-A49-005). This research was also partly supported by the Higher Education Sprout Project and Ministry of Education Yushan Scholar Program from the National Yang Ming Chiao Tung University and Ministry of Education, Taiwan. We are also grateful to the National Center for High-performance Computing for computer time and facilities.
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Tsai, MY., Lu, H.HS. (2022). Integration of Boolean and Bayesian Networks. In: Lu, H.HS., Schölkopf, B., Wells, M.T., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65902-1_9
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