Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.

ABSTRACT: Manually curating biomedical knowledge from publications is necessary to build a knowledge based service that provides highly precise and organized information to users. The process of retrieving relevant publications for curation, which is also known as document triage, is usually carried out by querying and reading articles in PubMed. However, this query-based method often obtains unsatisfactory precision and recall on the retrieved results, and it is difficult to manually generate optimal queries. To address this, we propose a machine-learning assisted triage method. We collect previously curated publications from two databases UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog, and used them as a gold-standard dataset for training deep learning models based on convolutional neural net

SUBMITTER: Lee K 

PROVIDER: S-EPMC6107285 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature