Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures
This study advances our mechanistic understanding of IAV host adaptation, identifies molecular determinants for zoonotic risk stratification, and establishes a scalable machine learning framework for predicting viral host tropism through nucleotide signature analysis, thereby enhancing surveillance strategies and informing preventive measures against emerging viral threats.
Authors
- Tingting Pei (ORCID: https://orcid.org/0000-0002-2789-4919)
- Chenglong Xiong (ORCID: https://orcid.org/0000-0003-4750-3572)
- Jianlin Zhuang
- Qi Chen (ORCID: https://orcid.org/0000-0002-6510-907X)
- Xiaoding He
- Shuiping Lu
- Zhan Zhang (ORCID: https://orcid.org/0000-0002-7915-1324)
- Xinyu Liu (ORCID: https://orcid.org/0000-0002-6776-9063)
- Weijie Chen (ORCID: https://orcid.org/0000-0002-9780-1167)
- Jiaxu Chen (ORCID: https://orcid.org/0000-0001-5768-7657)
- Su Zhuang (ORCID: https://orcid.org/0000-0001-9193-0032)
- Siqi Hu (ORCID: https://orcid.org/0000-0001-6564-9781)
- Jingjing Hu
Institutions
- Fudan University (CN)
- Shanghai Public Health Clinical Center (CN)
- Ministry of Education (KR)
- Shanghai Changning Mental Health Center (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1186/s12967-025-07569-x
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00