GPC: An expressive and tractable deep generative model for genetic variation data.
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| Title: | GPC: An expressive and tractable deep generative model for genetic variation data. |
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| Authors: | Anand P; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America., Liu A; Department of Computer Science, National University of Singapore, Singapore, Singapore., Dang M; Department of Computer Science, Stanford University, Stanford, California, United States of America., Fu B; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America., Wei X; Department of Computational Biology, Cornell University, Ithaca, New York, United States of America., Van den Broeck G; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America., Sankararaman S; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America.; Department of Human Genetics, University of California, Los Angeles, California, United States of America.; Department of Computational Medicine, University of California, Los Angeles, California, United States of America. |
| Source: | PLoS genetics [PLoS Genet] 2026 Oct 06; Vol. 22 (10), pp. e1012321. Date of Electronic Publication: 2026 Oct 06 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7404 (Electronic) Linking ISSN: 15537390 NLM ISO Abbreviation: PLoS Genet Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1553-7404 |
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| DOI: | 10.1371/journal.pgen.1012321 |
