Evaluating machine learning approaches for host prediction using H3 influenza genomic data
These models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.
Authors
- Hoc Tran (ORCID: https://orcid.org/0009-0008-4802-3427)
- Olaf Berke (ORCID: https://orcid.org/0000-0003-3537-0629)
- Zvonimir Poljak (ORCID: https://orcid.org/0000-0001-8621-3661)
- Nicole Ricker (ORCID: https://orcid.org/0000-0001-5706-5399)
Institutions
- University of Guelph (CA)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1371/journal.pone.0336142
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00