Cahuantzi, Roberto, Lythgoe, Katrina A., Hall, Ian, Pellis, Lorenzo, House, Thomas (2024) Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proceedings of the National Academy of Sciences, 121 (12) doi:10.1073/pnas.2317284121
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods | ||
| Journal | Proceedings of the National Academy of Sciences | ||
| Authors | Cahuantzi, Roberto | Author | |
| Lythgoe, Katrina A. | Author | ||
| Hall, Ian | Author | ||
| Pellis, Lorenzo | Author | ||
| House, Thomas | Author | ||
| Year | 2024 (March 19) | Volume | 121 |
| Issue | 12 | ||
| Publisher | Proceedings of the National Academy of Sciences | ||
| DOI | doi:10.1073/pnas.2317284121Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 17218097 | Long-form Identifier | mindat:1:5:17218097:9 |
| GUID | 0 | ||
| Full Reference | Cahuantzi, Roberto, Lythgoe, Katrina A., Hall, Ian, Pellis, Lorenzo, House, Thomas (2024) Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proceedings of the National Academy of Sciences, 121 (12) doi:10.1073/pnas.2317284121 | ||
| Plain Text | Cahuantzi, Roberto, Lythgoe, Katrina A., Hall, Ian, Pellis, Lorenzo, House, Thomas (2024) Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proceedings of the National Academy of Sciences, 121 (12) doi:10.1073/pnas.2317284121 | ||
| In | (2024, March) Proceedings of the National Academy of Sciences Vol. 121 (12) Proceedings of the National Academy of Sciences | ||
See Also
These are possibly similar items as determined by title/reference text matching only.
