A new University of Liverpool (UK) study could help scientists mitigate the future spread of zoonotic and livestock diseases caused by viruses. Researchers have used a form of artificial intelligence (AI) called machine-learning to predict more than 20,000 unknown associations between known viruses and susceptible mammalian species. The findings, which were published online on June 25, 2021 Nature Communications, could be used to help target disease surveillance programs. The open-access article is titled “Divide-and-Conquer: Machine-Learning Integrates Mammalian and Viral Traits with Network Features to Predict Virus-Mammal Associations.” Thousands of viruses are known to affect mammals, with recent estimates indicating that less than 1% of mammalian viral diversity has been discovered to date. Some of these viruses such as human and feline immunodeficiency viruses have a very narrow host range, whereas others such as rabies and West Nile viruses have very wide host ranges.
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