Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.

Journal: BMC genomics
PMID: 40269678

Abstract

BACKGROUND: Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments.

Authors

  • Sangwook Kim
    School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
  • Min-Ah Kim
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Bitgoeul Kim
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Jisu Lee
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Se-Kyung Jung
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Jonghong Kim
    School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
  • Ho-Young Chung
    Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Chung-Young Lee
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea. cylee87@knu.ac.kr.
  • Sungmoon Jeong
    Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea.

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