Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.

Rasmussen, Sophie Lund; Pertoldi, Cino; Macdonald, David W

Rasmussen, Sophie Lund; Pertoldi, Cino; Macdonald, David W.

Rasmussen SL; Wildlife Conservation Research Unit, The Recanati-Kaplan Centre, Department of Biology, University of Oxford, Tubney House, Abingdon Road, Tubney, Abingdon, OX13 5QL, UK. sophielundrasmussen@gmail.com.
Pertoldi C; Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, DK-9220, Aalborg, Denmark. sophielundrasmussen@gmail.com.
Macdonald DW; Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, DK-9220, Aalborg, Denmark.

Nat Commun ; 13(1): 5101, 2022 09 12.

Article in English | MEDLINE | ID: covidwho-2016702

Subject(s)

Animals, Wild; Machine Learning; Animals; SARS-CoV-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / Animals, Wild Type of study: Prognostic study Limits: Animals Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2022 Document Type: Article Affiliation country: S41467-022-32746-7

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