Sunkyu Kim

TL;DR: This article proposed BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora.

TL;DR: BEST, a biomedical entity search tool, is introduced, the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results.

TL;DR: Wang et al. as mentioned in this paper constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil.

TL;DR: The database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining and the new user interface supports diverse search options and graphic representation of fusion gene structure.

TL;DR: A PubMed knowledge graph (PKG) was constructed by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID ®, and identifying fine-grained affiliation data from MapAffil.