PubTator central: automated concept annotation for biomedical full text articles

By Chih-Hsuan Wei, Alexis Allot, Robert Leaman, Zhiyong Lu. Nucleic Acids Res., 2019

383 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/918d668556938b8cb45f257773b01d50eaba0586

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Heng Ji
2. Nikolaus Nova Parulian
3. Luyang Robby Huang
4. Lu Wang
5. Shuyang Cao
6. Mohit Bansal
7. David Wan

In Molecular Biology

1. Heng Ji
2. Lu Wang

Citations in the corpus

• Efficient Attentions for Long Document Summarization (2021, methodology): “This model can extract entities from general conversation contexts 2011 (BioNLP, 2011), Genia 2013 (BioNLP, 2013), and PubMed (Wei et al., 2019) datasets.”