TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution
The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leadi...
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sg-ntu-dr.10356-1706382023-09-25T02:30:09Z TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution Zhou, Binbin Zhou, Hang Zhang, Xue Xu, Xiaobin Chai, Yi Zheng, Zengwei Kot, Alex Chichung Zhou, Zhan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering SARS-CoV-2 Viral Evolution The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO. This work is supported by the National Natural Science Foundation of China (Grant No. 62102349), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LDT23H19011H19), and the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (Grant No. LHDMZ22H300002). 2023-09-25T02:30:09Z 2023-09-25T02:30:09Z 2023 Journal Article Zhou, B., Zhou, H., Zhang, X., Xu, X., Chai, Y., Zheng, Z., Kot, A. C. & Zhou, Z. (2023). TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution. Computers in Biology and Medicine, 152, 106264-. https://dx.doi.org/10.1016/j.compbiomed.2022.106264 0010-4825 https://hdl.handle.net/10356/170638 10.1016/j.compbiomed.2022.106264 36535209 2-s2.0-85145491747 152 106264 en Computers in Biology and Medicine © 2022 Elsevier Ltd. All rights reserved. |
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Engineering::Electrical and electronic engineering SARS-CoV-2 Viral Evolution Zhou, Binbin Zhou, Hang Zhang, Xue Xu, Xiaobin Chai, Yi Zheng, Zengwei Kot, Alex Chichung Zhou, Zhan TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
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The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Zhou, Binbin Zhou, Hang Zhang, Xue Xu, Xiaobin Chai, Yi Zheng, Zengwei Kot, Alex Chichung Zhou, Zhan |
format |
Article |
author |
Zhou, Binbin Zhou, Hang Zhang, Xue Xu, Xiaobin Chai, Yi Zheng, Zengwei Kot, Alex Chichung Zhou, Zhan |
author_sort |
Zhou, Binbin |
title |
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
title_short |
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
title_full |
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
title_fullStr |
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
title_full_unstemmed |
TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution |
title_sort |
tempo: a transformer-based mutation prediction framework for sars-cov-2 evolution |
publishDate |
2023 |
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https://hdl.handle.net/10356/170638 |
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1779156366657585152 |