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ORIGINAL RESEARCH article

Front. Microbiol.
Sec. Virology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1485748
This article is part of the Research Topic Research Advances and Challenges in Emerging and Re-Emerging Viral Diseases View all 7 articles

Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization

Provisionally accepted
Ziyu Liu Ziyu Liu 1Yi Shen Yi Shen 2Yunliang Jiang Yunliang Jiang 3*Hancan Zhu Hancan Zhu 4Hailong Hu Hailong Hu 1*Yanlei Kang Yanlei Kang 1*Ming Chen Ming Chen 2Zhong Li Zhong Li 1*
  • 1 School of Information Engineering, Huzhou University, Huzhou, China
  • 2 College of Life Sciences, Zhejiang University, Hangzhou, Jiangsu Province, China
  • 3 School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China
  • 4 School of Mathematics, Physics and Information, Shaoxing University, Shaoxing, China

The final, formatted version of the article will be published soon.

    Introduction: The evolution of SARS-CoV-2 has precipitated the emergence of new mutant strains, some exhibiting enhanced transmissibility and immune evasion capabilities, thus escalating the infection risk and diminishing vaccine efficacy. Given the continuous impact of SARS-CoV-2 mutations on global public health, the economy, and society, a profound comprehension of potential variations is crucial to effectively mitigate the impact of viral evolution. Yet, this task still faces considerable challenges. Methods: This study introduces DARSEP, a method based on Deep learning Associates with Reinforcement learning for SARS-CoV-2 Evolution Prediction, combined with self-game sequence optimization and RetNet-based model. Results: DARSEP accurately predicts evolutionary sequences and investigates the virus's evolutionary trajectory. It filters spike protein sequences with optimal fitness values from an extensive mutation space, selectively identifies those with a higher likelihood of evading immune detection, and devises a superior evolutionary analysis model for SARS-CoV-2 spike protein sequences. Comprehensive downstream task evaluations corroborate the model's efficacy in predicting potential mutation sites, elucidating SARS-CoV-2's evolutionary direction, and analyzing the development trends of Omicron variant strains through semantic changes. Conclusion: Overall, DARSEP enriches our understanding of the dynamic evolution of SARS-CoV-2 and provides robust support for addressing present and future epidemic challenges.

    Keywords: deep learning, SARS-CoV-2, variation and evolution analysis, self-game sequence optimization, DARSEP model

    Received: 24 Aug 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Liu, Shen, Jiang, Zhu, Hu, Kang, Chen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yunliang Jiang, School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China
    Hailong Hu, School of Information Engineering, Huzhou University, Huzhou, 313000, China
    Yanlei Kang, School of Information Engineering, Huzhou University, Huzhou, 313000, China
    Zhong Li, School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.