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Updated: Oct 31, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.

Tunca Doğan1,2,3,4, Heval Atas3, Vishal Joshi4

  • 1Department of Computer Engineering, Hacettepe University, Ankara 06800, Turkey.

Nucleic Acids Research
|June 28, 2021
PubMed
Summary

CROssBAR integrates diverse biological data using deep learning to predict relationships, creating interactive knowledge graphs for disease mechanism research. This system aids in understanding complex biological networks and potential drug targets.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Large-scale biological and biomedical data are crucial for understanding disease mechanisms and developing treatments.
  • Data fragmentation across resources and technologies hinders integrative multi-omics analysis.

Purpose of the Study:

  • To develop a novel methodology for integrating and representing diverse biological data.
  • To construct a comprehensive biological data resource, CROssBAR, to facilitate systems-level research.

Main Methods:

  • Integrated large-scale biological/biomedical data from multiple resources into a NoSQL database.
  • Employed deep learning to predict relationships between data entries, enriching the dataset.
  • Developed interactive knowledge graphs for on-the-fly visualization of complex biological networks based on user queries.

Main Results:

  • Created CROssBAR, a system integrating heterogeneous biological data, including genes, proteins, pathways, phenotypes, diseases, and drugs.
  • Generated biologically meaningful modules through rigorous analysis of enriched data.
  • Enabled easy-to-interpret, interactive knowledge graphs for systems-level research.

Conclusions:

  • CROssBAR provides a comprehensive platform for integrative analysis of multi-omics data.
  • The system facilitates the inference of biological mechanisms, gene-protein relationships, and disease associations.
  • CROssBAR knowledge graphs are expected to advance systems-level research and therapeutic development.