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Text-mining clinically relevant cancer biomarkers for curation into the CIViC database.
Lever, Jake; Jones, Martin R; Danos, Arpad M; Krysiak, Kilannin; Bonakdar, Melika; Grewal, Jasleen K; Culibrk, Luka; Griffith, Obi L; Griffith, Malachi; Jones, Steven J M.
Afiliação
  • Lever J; Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
  • Jones MR; University of British Columbia, Vancouver, BC, Canada.
  • Danos AM; Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
  • Krysiak K; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
  • Bonakdar M; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
  • Grewal JK; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
  • Culibrk L; Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
  • Griffith OL; Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
  • Griffith M; University of British Columbia, Vancouver, BC, Canada.
  • Jones SJM; Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
Genome Med ; 11(1): 78, 2019 12 03.
Article em En | MEDLINE | ID: mdl-31796060
ABSTRACT

BACKGROUND:

Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature.

METHODS:

To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase.

RESULTS:

We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications.

CONCLUSIONS:

Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http//bionlp.bcgsc.ca/civicmine/.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Bases de Dados Factuais / Mineração de Dados / Neoplasias Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Bases de Dados Factuais / Mineração de Dados / Neoplasias Idioma: En Ano de publicação: 2019 Tipo de documento: Article