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Wilke, Claus O.; Vieira, Luiz C. (2025) Mechanistic modeling or machine learning for detecting variants of concern: Why not both? Proceedings of the National Academy of Sciences, 122 (30). doi:10.1073/pnas.2513608122

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Reference TypeJournal (article/letter/editorial)
TitleMechanistic modeling or machine learning for detecting variants of concern: Why not both?
JournalProceedings of the National Academy of Sciences
AuthorsWilke, Claus O.Author
Vieira, Luiz C.Author
Year2025 (July 29)Volume122
Issue30
PublisherProceedings of the National Academy of Sciences
DOIdoi:10.1073/pnas.2513608122Search in ResearchGate
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Mindat Ref. ID18746095Long-form Identifiermindat:1:5:18746095:0
GUID0
Full ReferenceWilke, Claus O.; Vieira, Luiz C. (2025) Mechanistic modeling or machine learning for detecting variants of concern: Why not both? Proceedings of the National Academy of Sciences, 122 (30). doi:10.1073/pnas.2513608122
Plain TextWilke, Claus O.; Vieira, Luiz C. (2025) Mechanistic modeling or machine learning for detecting variants of concern: Why not both? Proceedings of the National Academy of Sciences, 122 (30). doi:10.1073/pnas.2513608122
In(2025, July) Proceedings of the National Academy of Sciences Vol. 122 (30). Proceedings of the National Academy of Sciences

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

M. Huot D. Wang J. Liu E. I. Shakhnovich Predicting high-fitness viral protein variants with Bayesian active learning and biophysics. Proc. Natl. Acad. Sci. U.S.A. 17 e2503742122 (2025).
L. C. Vieira M. L. Handojo C. O. Wilke Medium-sized protein language models perform well at transfer learning on realistic datasets. Sci. Rep. 15 21400 (2025).
Not Yet Imported: - journal-article : 10.7554/eLife.83442

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Not Yet Imported: - journal-article : 10.1016/j.cell.2020.08.012

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S. Gurev N. Youssef N. Jain D. S. Marks Sequence-based protein models for the prediction of mutations across priority viruses (ICLR 2025 Workshop on Generative and Experimental Perspectives for Biomolecular Design) (2025). https://openreview.net/forum?id=DvC6VL7TJK (Accessed 2 July 2025).
R. Sawhney et al. Fine-tuning protein language models unlocks the potential of underrepresented viral proteomes. bioRxiv [Preprint] (2025). https://doi.org/10.1101/2025.04.17.649224 (Accessed 2 July 2025).
Not Yet Imported: - journal-article : 10.1021/acscentsci.3c01275

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