Text-mining clinically relevant cancer biomarkers for curation into the CIViC database.

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 extr

SUBMITTER: Lever J 

PROVIDER: S-EPMC6891984 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature