代数拓扑和深度学习揭示了SARS-CoV-2的传染性增强。

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Communications in Information and Systems Pub Date : 2021-01-01 Epub Date: 2021-02-08 DOI:10.4310/cis.2021.v21.n1.a2

Jiahui Chen, Rui Wang, Guo-Wei Wei

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引用次数: 0

摘要

由2019冠状病毒病(COVID-19)引起的严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)已导致巨大的人员死亡和经济损失。SARS-CoV-2的传染性是病毒广泛传播的一个关键原因，但由于世界各地正在进行的基因组进化，其严格的实验测量基本上是不可能的。我们表明，人工智能(AI)和代数拓扑(AT)提供了一种准确和有效的替代实验确定病毒传染性。AI和AT分析表明，正在进行的突变使SARS-CoV-2更具传染性。

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SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused by coronavirus disease 2019 (COVID-19) has led to a tremendous human fatality and economic loss. SARS-CoV-2 infectivity is a key reason for the widespread viral transmission, but its rigorous experimental measurement is essentially impossible due to the ongoing genome evolution around the world. We show that artificial intelligence (AI) and algebraic topology (AT) offer an accurate and efficient alternative to the experimental determination of viral infectivity. AI and AT analysis indicates that the on-going mutations make SARS-CoV-2 more infectious.

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Communications in Information and Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-

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