Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds
Predictive models based on observed data could assist practitioners in linking the genetics of the PRRS virus with clinical impact in clinical settings. Models trained in this study show promise for PRRS clinical impact prediction.
Authors
- Davor Ojkić (ORCID: https://orcid.org/0000-0002-4429-4843)
- Zvonimir Poljak (ORCID: https://orcid.org/0000-0001-8621-3661)
- Amy L. Greer (ORCID: https://orcid.org/0000-0002-6366-0458)
- Terri L. O’Sullivan (ORCID: https://orcid.org/0000-0001-5687-0185)
- Robert Friendship (ORCID: https://orcid.org/0000-0003-4630-3878)
- Dylan John Melmer (ORCID: https://orcid.org/0000-0001-9215-0441)
Institutions
- Trent University (CA)
- University of Guelph (CA)
Publication Details
- Journal
- American Journal of Veterinary Research
- Published
- 2025-01-09
- DOI
- https://doi.org/10.2460/ajvr.24.10.0289
- Primary Topic
- Animal Virus Infections Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00