PubTator central: automated concept annotation for biomedical full text articles

PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants...

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Veröffentlicht in:Nucleic acids research Jg. 47; H. W1; S. W587 - W593
Hauptverfasser: Wei, Chih-Hsuan, Allot, Alexis, Leaman, Robert, Lu, Zhiyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 02.07.2019
Schlagworte:
Cell Line
Data Curation
Data Mining - methods
Disease
Genes
Genetic Variation
Humans
Proteins
PubMed
Software
User-Computer Interface
Web Server Issue
ISSN:0305-1048, 1362-4962, 1362-4962
Online-Zugang:Volltext
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Zusammenfassung:PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.
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SourceType-Scholarly Journals-1
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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkz389