WaveSeekerNet: accurate prediction of influenza A virus subtypes and host source using attention-based deep learning
WaveSeekerNet's superior performance, efficiency, and ability to flag potential cross-species transmission events highlight its potential for real-time surveillance and pandemic preparedness. This model represents a significant advancement in applying deep learning for IAV classification and holds promise for future epidemiological, veterinary studies, and public health interventions.
Authors
- Carson K. Leung (ORCID: https://orcid.org/0000-0002-7541-9127)
- Josip Rudar (ORCID: https://orcid.org/0000-0003-0484-8028)
- Oliver Lung (ORCID: https://orcid.org/0000-0002-0494-1620)
- Oksana Vernygora (ORCID: https://orcid.org/0000-0001-6444-3888)
- Chad Laing (ORCID: https://orcid.org/0000-0003-1162-257X)
- Huu-Tai Nguyen (ORCID: https://orcid.org/0000-0002-5858-9466)
- Graham W. Taylor (ORCID: https://orcid.org/0000-0001-5867-3652)
- David R. Lapen (ORCID: https://orcid.org/0000-0003-3363-6964)
- Nathaniel Lesperance
Institutions
- Canadian Food Inspection Agency (CA)
- Agriculture and Agri-Food Canada (CA)
- Canadian Science Centre for Human and Animal Health (CA)
- Vector Institute (CA)
- University of Manitoba (CA)
- University of Guelph (CA)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf089
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Canadian Institute for Advanced Research
- University of Manitoba
- Canadian Bee Research Fund
- Government of Canada
- Natural Sciences and Engineering Research Council of Canada