Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
A more comprehensive map of viral host ranges can help identify and mitigate zoonotic and animal-disease risks. A divide-and-conquer approach which separates viral, mammalian and network features predicts over 20,000 unknown associations between known viruses and susceptible mammalian species.
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/dd15b3a0cc0f427b9059ea3b379d9ee6 |
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| Sumario: | A more comprehensive map of viral host ranges can help identify and mitigate zoonotic and animal-disease risks. A divide-and-conquer approach which separates viral, mammalian and network features predicts over 20,000 unknown associations between known viruses and susceptible mammalian species. |
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