A machine-compiled database of genome-wide association studies

Volodymyr Kuleshov; Jialin Ding; Christopher Vo; Braden Hancock; Alexander Ratner; Yang Li; Christopher Ré; Serafim Batzoglou; Michael Snyder

doi:10.1038/s41467-019-11026-x

Nature Communications (Jul 2019)

A machine-compiled database of genome-wide association studies

Volodymyr Kuleshov,
Jialin Ding,
Christopher Vo,
Braden Hancock,
Alexander Ratner,
Yang Li,
Christopher Ré,
Serafim Batzoglou,
Michael Snyder

Affiliations

Volodymyr Kuleshov: Department of Computer Science, Stanford University
Jialin Ding: Department of Computer Science, Stanford University
Christopher Vo: Department of Computer Science, Stanford University
Braden Hancock: Department of Computer Science, Stanford University
Alexander Ratner: Department of Computer Science, Stanford University
Yang Li: Department of Medicine, University of Chicago
Christopher Ré: Department of Computer Science, Stanford University
Serafim Batzoglou: Department of Computer Science, Stanford University
Michael Snyder: Department of Genetics, Stanford University School of Medicine

DOI: https://doi.org/10.1038/s41467-019-11026-x
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 8

Abstract

Read online

Most databases of genotype-phenotype associations are manually curated. Here, Kuleshov et al. describe a machine curation system that extracts such relationships from the GWAS literature and synthesizes them into a structured knowledge base called GWASkb that can complement manually curated databases.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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