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This paper introduces literature-based discovery (LBD) in biomedicine, covering models, systems, and evaluation. It equips readers to understand LBD challenges and design effective LBD systems for applications like drug development.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Knowledge Discovery

Background:

  • Literature-based discovery (LBD) is crucial for extracting novel insights from biomedical literature.
  • Existing LBD systems vary in models and methodologies, necessitating a unifying framework.

Purpose of the Study:

  • To provide a comprehensive overview of literature-based discovery (LBD) in the biomedical domain.
  • To familiarize readers with modern and historical LBD models, system components, evaluation methodologies, and current trends.
  • To enable readers to distinguish LBD systems and design their own LBD applications.

Main Methods:

  • A unifying framework for LBD systems is described.
  • Various LBD models are presented, including co-occurrence, semantic, and distributional models.
  • Key components such as term representation, filtering, ranking, and results display are discussed.

Main Results:

  • Readers will gain familiarity with LBD challenges and methodologies.
  • Readers will be able to differentiate between various LBD systems and publications.
  • Readers will be equipped to design an LBD system for specific biomedical applications.

Conclusions:

  • This paper offers a high-level perspective on LBD, suitable for researchers and system designers.
  • Understanding the presented framework and models facilitates effective LBD system development.
  • The overview serves as a foundation for further exploration into advanced LBD techniques and applications.