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

Veterinary Science

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

M. Wardeh, M. S. C. Blagrove, et al.

This groundbreaking study conducted by Maya Wardeh, Marcus S. C. Blagrove, Kieran J. Sharkey, and Matthew Baylis reveals the potential of machine learning in uncovering over 20,000 unknown virus-mammal associations, highlighting a significant gap in our understanding of these relationships, especially among wild mammals and various viruses.

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Playback language: English
Abstract
This study addresses the limited knowledge of viral host ranges by applying a machine-learning approach to predict unknown virus-mammal associations. The researchers used a "divide-and-conquer" strategy, separating viral, mammalian, and network features into three perspectives, each independently predicting associations. The approach predicted over 20,000 unknown associations, suggesting a significant underestimation of current knowledge, particularly for wild mammals and viruses like lyssaviruses, bornaviruses, and rotaviruses.
Publisher
Nature Communications
Published On
Jun 25, 2021
Authors
Maya Wardeh, Marcus S. C. Blagrove, Kieran J. Sharkey, Matthew Baylis
Tags
viral host ranges
machine learning
virus-mammal associations
wild mammals
lyssaviruses
bornaviruses
rotaviruses

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