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Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

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Abstract

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.

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Authors and Affiliations

  1. School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China

    Sanrong Liu & Haifeng Wang

Authors
  1. Sanrong Liu
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  2. Haifeng Wang
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Corresponding author

Correspondence to Haifeng Wang .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Liu, S., Wang, H. (2019). Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_15

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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