Machine learning models guide viral discovery in museum bat collections

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• Maya M. Juman, Department of Veterinary Medicine
• Monday 24 November 2025, 12:30-13:00
• SS03 Seminar Room, Willam Gates building (Department of Computer Science and Technology).

If you have a question about this talk, please contact Sam Nallaperuma-Herzberg.

Natural history museum collections are valuable but underutilized resources for viral discovery, offering opportunities to test hypotheses about pathogen occurrence across space, time, and taxonomic groups. We developed trait-based machine learning models of bat host suitability to guide viral screening of 1821 tissues in a museum collection. Our coronavirus and paramyxovirus predictive models performed with 79% and 92% predictive accuracy, respectively, and we used these models to generate ranked lists of suspect “novel” host species for screening. For the first time, we recovered these viruses from archived museum tissues, confirming three novel coronavirus host species and three novel paramyxovirus host species (3% and 33% prediction success rate, respectively). These sequences included a SARS -like coronavirus from an Angolan bat collected in June 2019, suggesting that viruses with epidemic potential may be more widespread in sub-Saharan Africa than previously believed. This case study lays out a framework for using predictive machine learning models to unlock pathogen data hidden in historical specimens.

This talk is part of the Accelerate Lunchtime Seminar Series series.

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