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• W4385803243 abstract "Abstract The dynamic evolution of the SARS-CoV-2 virus is largely driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. In this study, we introduce a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIEST’s proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step forward in the utilization of deep-learning methodologies for anticipatory viral mutation analysis and pandemic response." @default.
• W4385803243 created "2023-08-15" @default.
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• W4385803243 date "2023-08-14" @default.
• W4385803243 modified "2023-10-02" @default.
• W4385803243 title "PRIEST - Predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information" @default.
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• W4385803243 doi "https://doi.org/10.1101/2023.08.11.552988" @default.
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