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Yu B, Xu JM, Li S, et al. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method. Oncotarget. 2017;8(46):80373-80392doi: 10.18632/oncotarget.21268.
Yu, B., Xu, J. M., Li, S., Chen, C., Chen, R. X., Wang, L., Zhang, Y., & Wang, M. H. (2017). Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method. Oncotarget, 8(46), 80373-80392. https://doi.org/10.18632/oncotarget.21268
Yu, Bin, et al. "Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method." Oncotarget vol. 8,46 (2017): 80373-80392. doi: https://doi.org/10.18632/oncotarget.21268
Yu B, Xu JM, Li S, Chen C, Chen RX, Wang L, Zhang Y, Wang MH. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method. Oncotarget. 2017 Sep 23;8(46):80373-80392. doi: 10.18632/oncotarget.21268. eCollection 2017 Oct 06. PMID: 29113310; PMCID: PMC5655205.
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Oncotarget. 2017 Sep 23;8(46):80373-80392. doi: 10.18632/oncotarget.21268. eCollection 2017 Oct 06.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Oncotarget

Bin Yu, Jia-Meng Xu, Shan Li, Cheng Chen, Rui-Xin Chen, Lei Wang, Yan Zhang, Ming-Hui Wang

Affiliations

  1. College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
  2. CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Science, University of Science and Technology of China, Hefei 230026, China.
  3. Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China.
  4. Key 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.
  5. College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

PMID: 29113310 PMCID: PMC5655205 DOI: 10.18632/oncotarget.21268

Abstract

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

Keywords: comprehensive score model; dynamic Bayesian network; gene regulatory networks; multiple time-delayed; network structure profiles

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare no conflicts of interest.

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