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Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm

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Abstract

Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs.

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Data availability and material

The reaction chains datasets used in this study are available at https://www.genome.jp. The DREAM3 datasets are available at https://www.synapse.org/#!Synapse: syn2853597.The Escherichia coli datasets are available at http://regulondb.ccg.unam.mx/inde x.jsp.

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Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 61902125), Natural Science Foundation of Hunan Province (2019JJ50187), and Scientific Research Project of Hunan Education Department (Grant No. 19C1788).

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Authors and Affiliations

  1. School of Computer Science, Xiangtan University, Xiangtan, 411105, China

    Wei Liu, Yi Jiang, Xingen Sun, Wenqing Gan & Huanrong Tang

  2. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China

    Wei Liu

  3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Li Peng

  4. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

    Qi Zhao

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  1. Wei Liu
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Contributions

WL and YJ participated in the design of the study, developed the code and wrote the manuscript. QZ and HT conceived and coordinated the study and helped to revise the manuscript. LP and XS analyzed the results and provided the constructive discussions. WG helped to revise the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qi Zhao or Huanrong Tang.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Liu, W., Jiang, Y., Peng, L. et al. Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm. Interdiscip Sci Comput Life Sci 14, 168–181 (2022). https://doi.org/10.1007/s12539-021-00478-9

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  • DOI: https://doi.org/10.1007/s12539-021-00478-9

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