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A machine-compiled database of genome-wide association studies.
Kuleshov, Volodymyr; Ding, Jialin; Vo, Christopher; Hancock, Braden; Ratner, Alexander; Li, Yang; Ré, Christopher; Batzoglou, Serafim; Snyder, Michael.
Afiliação
  • Kuleshov V; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA. kuleshov@cs.stanford.edu.
  • Ding J; Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA. kuleshov@cs.stanford.edu.
  • Vo C; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
  • Hancock B; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
  • Ratner A; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
  • Li Y; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
  • Ré C; Department of Medicine, University of Chicago, Chicago, IL, 60637, USA.
  • Batzoglou S; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
  • Snyder M; Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
Nat Commun ; 10(1): 3341, 2019 07 26.
Article em En | MEDLINE | ID: mdl-31350405
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Genéticas / Estudo de Associação Genômica Ampla Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Genéticas / Estudo de Associação Genômica Ampla Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article