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:	Maya Wardeh, Marcus S. C. Blagrove, Kieran J. Sharkey, Matthew Baylis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/dd15b3a0cc0f427b9059ea3b379d9ee6
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spelling	oai:doaj.org-article:dd15b3a0cc0f427b9059ea3b379d9ee62021-12-02T18:02:51ZDivide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations10.1038/s41467-021-24085-w2041-1723https://doaj.org/article/dd15b3a0cc0f427b9059ea3b379d9ee62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24085-whttps://doaj.org/toc/2041-1723A 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.Maya WardehMarcus S. C. BlagroveKieran J. SharkeyMatthew BaylisNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Maya Wardeh Marcus S. C. Blagrove Kieran J. Sharkey Matthew Baylis Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
description	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.
format	article
author	Maya Wardeh Marcus S. C. Blagrove Kieran J. Sharkey Matthew Baylis
author_facet	Maya Wardeh Marcus S. C. Blagrove Kieran J. Sharkey Matthew Baylis
author_sort	Maya Wardeh
title	Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
title_short	Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
title_full	Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
title_fullStr	Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
title_full_unstemmed	Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
title_sort	divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dd15b3a0cc0f427b9059ea3b379d9ee6
work_keys_str_mv	AT mayawardeh divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations AT marcusscblagrove divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations AT kieranjsharkey divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations AT matthewbaylis divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations
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Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations

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