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Related Experiment Video

Updated: Aug 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Clustering rare diseases within an ontology-enriched knowledge graph.

Jaleal Sanjak1, Qian Zhu1, Ewy A Mathé1

  • 1Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD.

Biorxiv : the Preprint Server for Biology
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Summary

This study developed a novel method for clustering rare diseases using knowledge graphs and node embeddings. The approach successfully identified 37 disease clusters, revealing shared genetic links and paving the way for drug repurposing and therapeutic development.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying shared characteristics among rare diseases is crucial for advancing drug repurposing and therapeutic strategies.
  • Current methods for disease classification may not fully capture the complex relationships between rare diseases.

Conclusions:

  • Node embeddings provide an effective method for clustering diseases within heterogeneous knowledge graphs.
  • The study uncovered semantically similar diseases and relevant gene enrichments, facilitating potential drug repurposing.
  • The developed pipeline is maintainable and adaptable for future updates and integration of new data sources.