What question did this study set out to answer?

The primary aim is to provide automated annotations for biomedical full-text articles to improve concept coverage and accessibility.

PubTator central: automated concept annotation for biomedical full text articles

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PubTator Central automates bioconcept annotation in biomedical articles, enhancing research and discovery capabilities.

The primary aim is to provide automated annotations for biomedical full-text articles to improve concept coverage and accessibility.
Developed a web service to annotate genes, diseases, and more from full-text biomedical articles.
Incorporated advanced text mining systems and deep learning for enhanced annotation accuracy.
Provided multiple download formats and synchronized updates with PubMed and PubMed Central.
Achieved significant increases in annotation accuracy and speed with new deep learning and server-side architecture.
Expanded concept coverage in the annotated articles, supporting various downstream applications.
Enabled a variety of new use cases in biocuration, gene prioritization, and literature-based knowledge discovery.

Wei et al. (2019) studied this question.

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