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Recent advances in biomedical literature mining

  • Sendong Zhao
  • , Chang Su
  • , Zhiyong Lu
  • , Fei Wang*
  • *Corresponding author for this work
  • Cornell University
  • National Institutes of Health

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Article numberbbaa057
JournalBriefings in Bioinformatics
Volume22
Issue number3
DOIs
StatePublished - 1 May 2021
Externally publishedYes

Keywords

  • Biomedical Literature Mining
  • Deep Learning
  • Natural Language Processing

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