View in EDS HTML Full Text PDF Full Text

GPC: An expressive and tractable deep generative model for genetic variation data.

Saved in:
Bibliographic Details
Title: GPC: An expressive and tractable deep generative model for genetic variation data.
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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 42837403
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: GPC: An expressive and tractable deep generative model for genetic variation data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Anand+P%22">Anand P</searchLink>; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Liu+A%22">Liu A</searchLink>; Department of Computer Science, National University of Singapore, Singapore, Singapore.<br /><searchLink fieldCode="AU" term="%22Dang+M%22">Dang M</searchLink>; Department of Computer Science, Stanford University, Stanford, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Fu+B%22">Fu B</searchLink>; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.<br /><searchLink fieldCode="AU" term="%22Wei+X%22">Wei X</searchLink>; Department of Computational Biology, Cornell University, Ithaca, New York, United States of America.<br /><searchLink fieldCode="AU" term="%22Van+den+Broeck+G%22">Van den Broeck G</searchLink>; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Sankararaman+S%22">Sankararaman S</searchLink>; 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.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101239074%22">PLoS genetics</searchLink> [PLoS Genet] 2026 Oct 06; Vol. 22 (10), pp. e1012321. <i>Date of Electronic Publication: </i>2026 Oct 06 (<i>Print Publication: </i>2026).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Public+Library+of+Science%22">Public Library of Science </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101239074 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>1553-7404 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2215537390%22">15537390 </searchLink><i>NLM ISO Abbreviation: </i>PLoS Genet <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=42837403
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1371/journal.pgen.1012321
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e1012321
    Titles:
      – TitleFull: GPC: An expressive and tractable deep generative model for genetic variation data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Anand P
      – PersonEntity:
          Name:
            NameFull: Liu A
      – PersonEntity:
          Name:
            NameFull: Dang M
      – PersonEntity:
          Name:
            NameFull: Fu B
      – PersonEntity:
          Name:
            NameFull: Wei X
      – PersonEntity:
          Name:
            NameFull: Van den Broeck G
      – PersonEntity:
          Name:
            NameFull: Sankararaman S
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 06
              M: 10
              Text: 2026 Oct 06
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1553-7404
          Numbering:
            – Type: volume
              Value: 22
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: PLoS genetics
              Type: main
ResultId 1