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Biomedical ontologies and knowledge graphs are crucial for integrating complex cancer data. This review of 141 studies shows their growing use in cancer research for data accessibility and AI-driven insights.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer research involves complex, heterogeneous data from multiple biomedical disciplines.
  • A holistic view of cancer is essential for precision medicine, requiring data integration.
  • Biomedical ontologies offer a solution for creating cohesive knowledge models.

Purpose of the Study:

  • To review the application of ontologies and knowledge graphs in cancer research.
  • To categorize the usage of these tools in published works.
  • To identify commonly used and emerging ontologies.

Main Methods:

  • Systematic literature review of 141 published works.
  • Categorization of studies based on ontology and knowledge graph application.
  • Analysis of ontology usage trends in cancer research.

Main Results:

  • Ontologies and knowledge graphs are increasingly adopted in biomedical and cancer research.
  • Studies were categorized under 14 hierarchical themes based on their application.
  • Identified trends in the use of established and novel ontologies.

Conclusions:

  • Ontologies enhance data accessibility, interoperability, and integration in cancer research.
  • Knowledge graphs facilitate AI applications for unlocking new insights from complex data.
  • The integration of ontologies and knowledge graphs supports a holistic approach to cancer research and precision medicine.