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Disease variant prediction with deep generative models of evolutionary data.

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Bibliographic Details
Title: Disease variant prediction with deep generative models of evolutionary data.
Authors: Frazer J; Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA., Notin P; OATML Group, Department of Computer Science, University of Oxford, Oxford, UK., Dias M; Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA., Gomez A; OATML Group, Department of Computer Science, University of Oxford, Oxford, UK., Min JK; Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA., Brock K; Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA., Gal Y; OATML Group, Department of Computer Science, University of Oxford, Oxford, UK. yarin.gal@cs.ox.ac.uk., Marks DS; Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA. debbie@hms.harvard.edu.; Broad Institute of Harvard and MIT, Cambridge, MA, USA. debbie@hms.harvard.edu.
Source: Nature [Nature] 2021 Nov; Vol. 599 (7883), pp. 91-95. Date of Electronic Publication: 2021 Oct 27.
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1476-4687
DOI:10.1038/s41586-021-04043-8