Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
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Author / Creator
Guo, Qian , Li, Mo , Wang, Chunhui , Guo, Jinyuan , Jiang, Xiaoqing , Tan, Jie , Wu, Shufang , Wang, Peihong , Xiao, Tingting , Zhou, Man , Fang, Zhencheng , Xiao, Yonghong and Zhu, Huaiqiu
Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebr...
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Full title
Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
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TN_cdi_doaj_primary_oai_doaj_org_article_f0f9e0c0db64499c92721fc4f375dd3b
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f0f9e0c0db64499c92721fc4f375dd3b
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-96903-6
