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      LitCovid: an open database of COVID-19 literature

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      Author(s): , ,
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid ( https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

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          BioBERT: a pre-trained biomedical language representation model for biomedical text mining

          Jinhyuk Lee, Wonjin Yoon, Sungdong Kim … (2019)
          Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce 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. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
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            PubTator: a web-based text mining tool for assisting biocuration

            Chih-Hsuan Wei, Hung-Yu Kao, Zhiyong Lu (2013)
            Manually curating knowledge from biomedical literature into structured databases is highly expensive and time-consuming, making it difficult to keep pace with the rapid growth of the literature. There is therefore a pressing need to assist biocuration with automated text mining tools. Here, we describe PubTator, a web-based system for assisting biocuration. PubTator is different from the few existing tools by featuring a PubMed-like interface, which many biocurators find familiar, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results. Through a formal evaluation with two external user groups, PubTator was shown to be capable of improving both the efficiency and accuracy of manual curation. PubTator is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/.
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              PubTator central: automated concept annotation for biomedical full text articles

              Chih-Hsuan Wei, Alexis Allot, Robert Leaman … (2019)
              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.
              Article 0 comments Cited 155 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Qingyu Chen:
                ORCID: http://orcid.org/0000-0002-6036-1516
                Alexis Allot
                Zhiyong Lu:
                ORCID: http://orcid.org/0000-0001-9998-916X
                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 08 January 2021
                Publication date (Electronic): 09 November 2020
                Publication date PMC-release: 09 November 2020
                Volume: 49
                Issue: D1
                Pages: D1534-D1540
                Affiliations
                National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH) , Bethesda, MD 20892, USA
                National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH) , Bethesda, MD 20892, USA
                National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH) , Bethesda, MD 20892, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 301 594 7089; Fax: +1 301 480 2290; Email: zhiyong.lu@ 123456nih.gov

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                http://orcid.org/0000-0002-6036-1516
                http://orcid.org/0000-0001-9998-916X
                Article
                Publisher ID: gkaa952
                DOI: 10.1093/nar/gkaa952
                PMC ID: 7778958
                PubMed ID: 33166392
                SO-VID: 12649ce6-44bb-4760-91a7-f8c26ab01d54
                Copyright © Published by Oxford University Press on behalf of Nucleic Acids Research 2020.
                License:

                This work is written by (a) US Government employee(s) and is in the public domain in the US.

                History
                Date accepted : 08 October 2020
                Date revision received : 02 October 2020
                Date received : 15 August 2020
                Page count
                Pages: 7
                Funding
                Funded by: Intramural Research Program of the National Library of Medicine;
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Categories
                Subject: AcademicSubjects/SCI00010
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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