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Related Experiment Videos

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression

Xiangying Jiang1, Martin Ringwald2, Judith Blake2

  • 1Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, Newark, DE, USA.

Database : the Journal of Biological Databases and Curation
|April 3, 2017
PubMed
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This study introduces an automated document classification method to help curators identify relevant publications for the Gene Expression Database (GXD). Incorporating image captions significantly improves classification accuracy for biomedical research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The Gene Expression Database (GXD) requires manual curation of numerous biomedical publications.
  • Automatic document classification is crucial for efficient annotation workflows in biomedical research.

Purpose of the Study:

  • To develop an effective and efficient document classification scheme to support GXD curators.
  • To improve the identification of publications relevant to mouse gene expression.

Main Methods:

  • Implemented a classification scheme using readily available tools and feature selection.
  • Utilized text from titles, abstracts, and image captions for classification.
  • Evaluated the method on a dataset of over 25,000 PubMed abstracts.

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Main Results:

  • The proposed classification approach demonstrated robustness and effectiveness.
  • Integrating information from image captions significantly enhanced classification performance compared to using title and abstract alone.
  • Image captions serve as a valuable data source for subject-specific relevance determination.

Conclusions:

  • The developed classification method effectively assists GXD curators in identifying relevant literature.
  • Image caption analysis is a powerful strategy for improving the accuracy of biomedical document classification.
  • This approach enhances the efficiency of maintaining comprehensive biological databases.