Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.
作者信息
Blagrove Marcus S C, Baylis Matthew, Wardeh Maya
机构信息
Department of Evolution, Ecology, and Behaviour, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK.
Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool Science Park IC2 Building, 146 Brownlow Hill, Liverpool, L3 5RF, UK.
出版信息
Nat Commun. 2022 Sep 12;13(1):5102. doi: 10.1038/s41467-022-32747-6.
Abstract
摘要
相似文献
1
Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.回复：新型冠状病毒宿主的机器学习预测需谨慎，因为这可能影响野生动物保护。
Nat Commun. 2022 Sep 12;13(1):5102. doi: 10.1038/s41467-022-32747-6.
2
Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.对新型冠状病毒宿主进行机器学习预测需谨慎，因为这可能会影响野生动物保护。
Nat Commun. 2022 Sep 12;13(1):5101. doi: 10.1038/s41467-022-32746-7.
3
Overview of Bat and Wildlife Coronavirus Surveillance in Africa: A Framework for Global Investigations.非洲蝙蝠和野生动物冠状病毒监测概述：全球调查框架
Viruses. 2021 May 18;13(5):936. doi: 10.3390/v13050936.
4
No Evidence of Coronaviruses or Other Potentially Zoonotic Viruses in Sunda pangolins (Manis javanica) Entering the Wildlife Trade via Malaysia.在通过马来西亚进入野生物贸易的巽他穿山甲（Manis javanica）中未发现冠状病毒或其他潜在的人畜共患病毒。
Ecohealth. 2020 Sep;17(3):406-418. doi: 10.1007/s10393-020-01503-x. Epub 2020 Nov 23.
5
Evidence of SARS-CoV-2 Related Coronaviruses Circulating in Sunda pangolins () Confiscated From the Illegal Wildlife Trade in Viet Nam.越南从非法野生动物贸易中查获的马来穿山甲（）中存在与 SARS-CoV-2 相关的冠状病毒。
Front Public Health. 2022 Mar 9;10:826116. doi: 10.3389/fpubh.2022.826116. eCollection 2022.
6
Machine learning for tracking illegal wildlife trade on social media.利用机器学习追踪社交媒体上的非法野生动物贸易
Nat Ecol Evol. 2018 Mar;2(3):406-407. doi: 10.1038/s41559-018-0466-x.
7
Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?穿山甲是否是 2019 新型冠状病毒（SARS-CoV-2）的中间宿主？
PLoS Pathog. 2020 May 14;16(5):e1008421. doi: 10.1371/journal.ppat.1008421. eCollection 2020 May.
8
A framework for investigating illegal wildlife trade on social media with machine learning.利用机器学习调查社交媒体非法野生动物贸易的框架。
Conserv Biol. 2019 Feb;33(1):210-213. doi: 10.1111/cobi.13104. Epub 2018 Nov 14.
9
Cross-species transmission, evolution and zoonotic potential of coronaviruses.冠状病毒的跨物种传播、进化和人畜共患潜力。
Front Cell Infect Microbiol. 2023 Jan 6;12:1081370. doi: 10.3389/fcimb.2022.1081370. eCollection 2022.
10
Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.生物数据挖掘和机器学习技术在检测和诊断新型冠状病毒 (COVID-19) 中的作用：系统评价。
J Med Syst. 2020 May 25;44(7):122. doi: 10.1007/s10916-020-01582-x.
引用本文的文献
1
Identifying life-history patterns along the fast-slow continuum of mammalian viral carriers.识别哺乳动物病毒携带者在快慢连续体上的生活史模式。
R Soc Open Sci. 2024 Jul 24;11(7):231512. doi: 10.1098/rsos.231512. eCollection 2024 Jul.
本文引用的文献
1
Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.对新型冠状病毒宿主进行机器学习预测需谨慎，因为这可能会影响野生动物保护。
Nat Commun. 2022 Sep 12;13(1):5101. doi: 10.1038/s41467-022-32746-7.
2
Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.优化预测模型，优先在人畜共患病储库中发现病毒。
Lancet Microbe. 2022 Aug;3(8):e625-e637. doi: 10.1016/S2666-5247(21)00245-7. Epub 2022 Jan 10.
3
Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.分而治之：机器学习将哺乳动物和病毒特征与网络特征相结合，以预测病毒-哺乳动物的关联。
Nat Commun. 2021 Jun 25;12(1):3954. doi: 10.1038/s41467-021-24085-w.
4
Predicting mammalian hosts in which novel coronaviruses can be generated.预测新型冠状病毒可能产生的哺乳动物宿主。
Nat Commun. 2021 Feb 16;12(1):780. doi: 10.1038/s41467-021-21034-5.
5
Predicting mammalian species at risk of being infected by SARS-CoV-2 from an ACE2 perspective.从 ACE2 角度预测易感染 SARS-CoV-2 的哺乳动物物种。
Sci Rep. 2021 Jan 18;11(1):1702. doi: 10.1038/s41598-020-80573-x.
6
Susceptibility of Raccoon Dogs for Experimental SARS-CoV-2 Infection.貉对实验性 SARS-CoV-2 感染的易感性。
Emerg Infect Dis. 2020 Dec;26(12):2982-2985. doi: 10.3201/eid2612.203733. Epub 2020 Oct 22.
7
Broad host range of SARS-CoV-2 and the molecular basis for SARS-CoV-2 binding to cat ACE2.严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的广泛宿主范围以及SARS-CoV-2与猫血管紧张素转换酶2（ACE2）结合的分子基础。
Cell Discov. 2020 Sep 29;6:68. doi: 10.1038/s41421-020-00210-9. eCollection 2020.
8
Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates.通过比较和结构分析脊椎动物 ACE2 预测 SARS-CoV-2 的广泛宿主范围。
Proc Natl Acad Sci U S A. 2020 Sep 8;117(36):22311-22322. doi: 10.1073/pnas.2010146117. Epub 2020 Aug 21.
9
Spike protein recognition of mammalian ACE2 predicts the host range and an optimized ACE2 for SARS-CoV-2 infection.刺突蛋白对哺乳动物 ACE2 的识别预测了宿主范围和用于 SARS-CoV-2 感染的优化 ACE2。
Biochem Biophys Res Commun. 2020 May 21;526(1):165-169. doi: 10.1016/j.bbrc.2020.03.047. Epub 2020 Mar 19.
10
Similarity network fusion for aggregating data types on a genomic scale.基于基因组尺度聚合数据类型的相似网络融合。
Nat Methods. 2014 Mar;11(3):333-7. doi: 10.1038/nmeth.2810. Epub 2014 Jan 26.
Zotero 插件