Hidden challenges in evaluating spillover risk of zoonotic viruses using machine learning models
Our study provides a comprehensive benchmark for viral infectivity prediction models and highlights unresolved issues in fully exploiting machine learning to prepare for future zoonotic threats.
Authors
- Michiaki Hamada (ORCID: https://orcid.org/0000-0001-9466-1034)
- Tadaki Suzuki (ORCID: https://orcid.org/0000-0002-3820-9542)
- Junna Kawasaki (ORCID: https://orcid.org/0000-0002-6609-5300)
Institutions
- Waseda University (JP)
- Chiba University (JP)
- National Institute of Infectious Diseases (JP)
- National Institute of Advanced Industrial Science and Technology (JP)
- Nippon Medical School (JP)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1038/s43856-025-00903-w
- Citations
- 9
- Primary Topic
- Zoonotic diseases and public health
- Type
- article
- Field-Weighted Citation Impact
- 16.91
Funders
- University of Tokyo
- Japan Society for the Promotion of Science
- Institute of Genetics
- Precursory Research for Embryonic Science and Technology