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Inference of Gene Regulatory Network from Time Series Expression Data by Combining Local Geometric Similarity and Multivariate Regression

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Abstract

Gene regulatory network (GRN) plays a pivotal role in cells. Existing high-throughput experiments facilitate abundant time-series expression data to reconstruct GRN to gain insight into the mechanisms of diverse biological procedure when organisms response to external changing conditions. However, many proposed approaches do not effectively elucidate local dynamic temporal information and time delay based on time-series expression data. In this paper, we introduce local geometric similarity and multivariate regression (LESME) to infer gene regulatory networks from time-course gene expression data. We simultaneously consider the local shape of time series and global multivariate regression to effectively detect the gene regulation. Moreover, LESME combines adaptive sliding window technique and grey relational analysis to track and capture local geometric similarity for improving the quality of global network inference. We incorporate the local and global contributions to reconstruct the GRN. LESME outperforms eight state-of-the-art methods on DREAM3 and DREAM4 in-silico challenges and achieves a meaningful result on hepatocellular carcinoma gene expression data.

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Acknowledgements

This work was partially supported by National Key Research and Development Program of China (No. 2020YFA0712402); National Natural Science Foundation of China (NSFC) (61973190); Natural Science Foundation of Shandong Province of China (ZR2020ZD25) and Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project, 2019JZZY010423); the Program of Qilu Young Scholars of Shandong University.

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 50061, Shandong, China

    Guangyi Chen & Zhi-Ping Liu

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  1. Guangyi Chen
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  2. Zhi-Ping Liu
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Correspondence to Zhi-Ping Liu .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Chen, G., Liu, ZP. (2021). Inference of Gene Regulatory Network from Time Series Expression Data by Combining Local Geometric Similarity and Multivariate Regression. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84531-5

  • Online ISBN: 978-3-030-84532-2

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