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Learning from pre-pandemic data to forecast viral antibody escape
View ORCID ProfileNicole N. Thadani, View ORCID ProfileSarah Gurev, View ORCID ProfilePascal Notin, View ORCID ProfileNoor Youssef, Nathan J. Rollins, View ORCID ProfileChris Sander, View ORCID ProfileYarin Gal, View ORCID ProfileDebora S. Marks
doi: https://doi.org/10.1101/2022.07.21.501023
Nicole N. Thadani
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
Sarah Gurev
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
Pascal Notin
3OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
Noor Youssef
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
Nathan J. Rollins
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
Chris Sander
4Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
5Department of Cell Biology, Harvard Medical School, Boston, MA, USA
6Broad Institute of Harvard and MIT, Cambridge, MA, USA
Yarin Gal
3OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
Debora S. Marks
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
6Broad Institute of Harvard and MIT, Cambridge, MA, USA
Posted July 22, 2022.
Learning from pre-pandemic data to forecast viral antibody escape
Nicole N. Thadani, Sarah Gurev, Pascal Notin, Noor Youssef, Nathan J. Rollins, Chris Sander, Yarin Gal, Debora S. Marks
bioRxiv 2022.07.21.501023; doi: https://doi.org/10.1101/2022.07.21.501023
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