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

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.

Bibliographic Details
Main Authors: Maya Wardeh, Marcus S. C. Blagrove, Kieran J. Sharkey, Matthew Baylis
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-24085-w
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spelling doaj-dd15b3a0cc0f427b9059ea3b379d9ee62021-06-27T11:13:17ZengNature Publishing GroupNature Communications2041-17232021-06-0112111510.1038/s41467-021-24085-wDivide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associationsMaya Wardeh0Marcus S. C. Blagrove1Kieran J. Sharkey2Matthew Baylis3Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of LiverpoolDepartment of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary & Ecological Sciences, University of LiverpoolDepartment of Mathematical Sciences, University of LiverpoolDepartment of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of LiverpoolA 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.https://doi.org/10.1038/s41467-021-24085-w
collection DOAJ
language English
format Article
sources DOAJ
author Maya Wardeh
Marcus S. C. Blagrove
Kieran J. Sharkey
Matthew Baylis
spellingShingle 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
Nature Communications
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 Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-06-01
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.
url https://doi.org/10.1038/s41467-021-24085-w
work_keys_str_mv AT mayawardeh divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations
AT marcusscblagrove divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations
AT kieranjsharkey divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations
AT matthewbaylis divideandconquermachinelearningintegratesmammalianandviraltraitswithnetworkfeaturestopredictvirusmammalassociations
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