UniBind: a novel artificial intelligence-based prediction model for SARS-CoV-2 infectivity and variant evolution
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics
Link
https://www.nature.com/articles/s41392-023-01691-2.pdf
Reference5 articles.
1. Wang, G. et al. Deep-learning-enabled protein–protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution. Nat. Med. 29, 2007–2018 (2023).
2. Cao, Y. et al. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature 614, 521–529 (2023).
3. He, P. et al. SARS-CoV-2 Delta and Omicron variants evade population antibody response by mutations in a single spike epitope. Nat. Microbiol. 7, 1635–1649 (2022).
4. Taft, J. M. et al. Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. Cell 185, 4008–4022.e4014 (2022).
5. Han, W. et al. Predicting the antigenic evolution of SARS-COV-2 with deep learning. Nat. Commun. 14, 1–14 (2023).
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