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 and Michael Snyder
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Volodymyr Kuleshov: Stanford University
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

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-11026-x

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