Literature DB >> 31114887

PubTator central: automated concept annotation for biomedical full text articles.

Chih-Hsuan Wei1, Alexis Allot1, Robert Leaman1, Zhiyong Lu1.   

Abstract

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. Published by Oxford University Press on behalf of Nucleic Acids Research 2019.

Entities:  

Year:  2019        PMID: 31114887      PMCID: PMC6602571          DOI: 10.1093/nar/gkz389

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  48 in total

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8.  Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.

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9.  Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics.

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10.  Untangling the genetic link between type 1 and type 2 diabetes using functional genomics.

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