IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-11026-x.html
   My bibliography  Save this article

A machine-compiled database of genome-wide association studies

Author

Listed:
  • Volodymyr Kuleshov

    (Stanford University
    Stanford University School of Medicine)

  • Jialin Ding

    (Stanford University)

  • Christopher Vo

    (Stanford University)

  • Braden Hancock

    (Stanford University)

  • Alexander Ratner

    (Stanford University)

  • Yang Li

    (University of Chicago)

  • Christopher Ré

    (Stanford University)

  • Serafim Batzoglou

    (Stanford University)

  • Michael Snyder

    (Stanford University School of Medicine)

Abstract

Tens of thousands of genotype-phenotype associations have been discovered to date, yet not all of them are easily accessible to scientists. Here, we describe GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms. Our information extraction system helps curators by automatically collecting over 6,000 associations from open-access publications with an estimated recall of 60–80% and with an estimated precision of 78–94% (measured relative to existing manually curated knowledge bases). This system represents a fully automated GWAS curation effort and is made possible by a paradigm for constructing machine learning systems called data programming. Our work represents a step towards making the curation of scientific literature more efficient using automated systems.

Suggested Citation

  • Volodymyr Kuleshov & Jialin Ding & Christopher Vo & Braden Hancock & Alexander Ratner & Yang Li & Christopher Ré & Serafim Batzoglou & Michael Snyder, 2019. "A machine-compiled database of genome-wide association studies," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11026-x
    DOI: 10.1038/s41467-019-11026-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-11026-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-019-11026-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Menta, Giorgia & Lepinteur, Anthony & Clark, Andrew E. & Ghislandi, Simone & D'Ambrosio, Conchita, 2023. "Maternal genetic risk for depression and child human capital," Journal of Health Economics, Elsevier, vol. 87(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11026-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.