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.
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DOI：
10.1038/s41467-021-24085-w
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发表时间：
2021-06-25
影响因子：
16.6
通讯作者：
Baylis M
中科院分区：
文献类型：
--
作者：
Wardeh M;Blagrove MSC;Sharkey KJ;Baylis M
我们对病毒宿主范围的了解仍然有限。通过识别已知病毒的未知宿主来完成这一工作是一个重要的研究目标，可以帮助识别和减轻人畜共患病和动物疾病的风险，例如从动物宿主到人类的溢出。为了解决这一知识差距，我们采用了一种分而治之的方法，将病毒，哺乳动物和网络特征分为三个独特的视角，每个视角独立预测关联以提高预测能力。我们的方法预测了已知病毒和易感哺乳动物物种之间的20，000多个未知关联，这表明目前的知识低估了野生和半驯化哺乳动物中的关联数量，低估了4.3倍，以及病毒的平均潜在哺乳动物宿主范围，低估了3.2倍。特别是，我们的研究结果突出了重要的人畜共患病和驯养哺乳动物病毒的野生水库的显着知识差距：特别是狂犬病毒，博纳病毒和轮状病毒。一个更全面的病毒宿主范围图可以帮助识别和减轻人畜共患病和动物疾病的风险。一种将病毒、哺乳动物和网络特征分开的分而治之的方法预测了已知病毒和易感哺乳动物物种之间超过20，000种未知的关联。
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals’ viruses: specifically, lyssaviruses, bornaviruses and rotaviruses. 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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影响因子：
14.9
作者：
Federhen S
通讯作者：
Federhen S
DOI：
10.1098/rspb.2008.0284
发表时间：
2008-07-22
影响因子：
4.7
作者：
Davies, T. Jonathan;Pedersen, Amy B.
通讯作者：
Pedersen, Amy B.
影响因子：
11.8
作者：
Cui J;Han N;Streicker D;Li G;Tang X;Shi Z;Hu Z;Zhao G;Fontanet A;Guan Y;Wang L;Jones G;Field HE;Daszak P;Zhang S
通讯作者：
Zhang S
影响因子：
4.3
作者：
Dallas T;Park AW;Drake JM
通讯作者：
Drake JM
影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP