Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method
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Bin Yu1,2,3,*, Jia-Meng Xu1,3,*, Shan Li1,3,*, Cheng Chen1,3, Rui-Xin Chen1,3, Lei Wang4, Yan Zhang3,5 and Ming-Hui Wang1,3
1College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
2CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Science, University of Science and Technology of China, Hefei 230026, China
3Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
4Key Laboratory of Eco-chemical Engineering, Ministry of Education, Laboratory of Inorganic Synthesis and Applied Chemistry, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
5College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
*These authors contributed equally to this work and are joint First Authors
Bin Yu, email: email@example.com
Keywords: gene regulatory networks, multiple time-delayed, dynamic Bayesian network, comprehensive score model, network structure profiles
Received: July 20, 2017 Accepted: August 27, 2017 Published: September 23, 2017
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.
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