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Recent advances in biomedical literature mining

Briefings in Bioinformatics, 2021-05, Vol.22 (3) [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;2020. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://academic.oup.com/journals/pages/coronavirus . ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbaa057 ;PMID: 32422651

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  • Title:
    Recent advances in biomedical literature mining
  • Author: Zhao, Sendong ; Su, Chang ; Lu, Zhiyong ; Wang, Fei
  • Subjects: Algorithms ; Biomedical Research ; Data Mining - methods ; Medical Informatics ; Method Review ; Publishing
  • Is Part Of: Briefings in Bioinformatics, 2021-05, Vol.22 (3)
  • Description: The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.
  • Publisher: England: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1467-5463
    EISSN: 1477-4054
    DOI: 10.1093/bib/bbaa057
    PMID: 32422651
  • Source: Coronavirus Research Database
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