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Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations

Author

Listed:
  • Maya Wardeh

    (Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool
    Department of Mathematical Sciences, University of Liverpool)

  • Marcus S. C. Blagrove

    (Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool)

  • Kieran J. Sharkey

    (Department of Mathematical Sciences, University of Liverpool)

  • Matthew Baylis

    (Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool
    Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool)

Abstract

Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals’ viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.

Suggested Citation

  • Maya Wardeh & Marcus S. C. Blagrove & Kieran J. Sharkey & Matthew Baylis, 2021. "Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24085-w
    DOI: 10.1038/s41467-021-24085-w
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    Cited by:

    1. Marcus S. C. Blagrove & Matthew Baylis & Maya Wardeh, 2022. "Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation," Nature Communications, Nature, vol. 13(1), pages 1-3, December.

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