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Inference Method for Reconstructing Regulatory Networks Using Statistical Path-Consistency Algorithm and Mutual Information

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

The advances of high-throughout technologies have produced huge amount of data regarding gene expressions or protein activities under various experimental conditions. The reverse-engineering of regulatory networks using these datasets is one of the top important research topics in computational biology. Although substantial efforts have been contributed to design effective inference methods, there are still a number of significant challenges to deal with the weak correlations between the observation data and the dependence of network structure on the order of variables in the systems. To address these issues, this work proposes a novel statistical approach to infer the structure of regulatory networks. Instead of using one single variable order, we generate a number of variable orders and then obtain different networks based on these orders. The weight of each edge for connecting genes/proteins is determined by the statistical measures based on the generated networks using different variable orders. Our proposed algorithm is evaluated by using the golden standard networks in Dream challenges and a cell signalling transduction pathway by using experimental data. Inference results suggest that our proposed algorithm is an effective approach for the reverse-engineering of regulatory networks with better accuracy.

This work is supported by the National Natural Science Foundation of China (Grant number: 11931019, 11871238), and the Science Foundation of Wuhan Institute of Technology (Grant number K202047).

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Author information

Authors and Affiliations

  1. School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, China

    Yan Yan

  2. School of Mathematics and Statistics, Central China Normal University, Wuhan, China

    Xinan Zhang

  3. School of Mathematics, Monash University, Melbourne, Australia

    Tianhai Tian

Authors
  1. Yan Yan
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  2. Xinan Zhang
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  3. Tianhai Tian
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Corresponding author

Correspondence to Tianhai Tian .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Yan, Y., Zhang, X., Tian, T. (2020). Inference Method for Reconstructing Regulatory Networks Using Statistical Path-Consistency Algorithm and Mutual Information. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-60802-6

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