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Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient

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  • Ziqi Liu

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China)

  • Ziqiao Yin

    (Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Peng Cheng Laboratory, Shenzhen 518055, China
    Zhongguancun Laboratory, Beijing 100191, China)

  • Zhilong Mi

    (Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Peng Cheng Laboratory, Shenzhen 518055, China)

  • Binghui Guo

    (Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
    Peng Cheng Laboratory, Shenzhen 518055, China
    Zhongguancun Laboratory, Beijing 100191, China)

Abstract

As the number of COVID-19 cases increases, the long-COVID symptoms become the focus of clinical attention. Based on the statistical analysis of long-COVID symptoms in European and Chinese populations, this study proposes the path module correlation coefficient, which can estimate the correlation between two modules in a network, to evaluate the correlation between SARS-CoV-2 infection and long-COVID symptoms, providing a theoretical support for analyzing the frequency of long-COVID symptoms in European and Chinese populations. The path module correlation coefficients between specific COVID-19-related genes in the European and Chinese populations and genes that may induce long-COVID symptoms were calculated. The results showed that the path module correlation coefficients were completely consistent with the frequency of long-COVID symptoms in the Chinese population, but slightly different in the European population. Furthermore, the cathepsin C (CTSC) gene was found to be a potential COVID-19-related gene by a path module correlation coefficient correction rate. Our study can help to explore other long-COVID symptoms that have not yet been discovered and provide a new perspective to research this syndrome. Meanwhile, the path module correlation coefficient correction rate can help to find more species-specific genes related to COVID-19 in the future.

Suggested Citation

  • Ziqi Liu & Ziqiao Yin & Zhilong Mi & Binghui Guo, 2023. "Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1368-:d:1094519
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    References listed on IDEAS

    as
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    3. Dmitry Grebennikov & Antonina Karsonova & Marina Loguinova & Valentina Casella & Andreas Meyerhans & Gennady Bocharov, 2022. "Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis," Mathematics, MDPI, vol. 10(17), pages 1-27, September.
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