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Related Experiment Video

Updated: May 28, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Increased discoverability of rare disease datasets through knowledge graph integration.

Ian Braun¹, Emily Hartley¹, Daniel Olson¹

¹Data Collaboration Center, Critical Path Institute, Tucson, AZ 85718, United States.

|February 10, 2025

Summary

This study enhances rare disease dataset discoverability by integrating patient data with biological associations. This creates a knowledge graph (KG) for improved semantic search and data summarization.

Keywords:

knowledge graph ontology rare disease

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Area of Science:

Bioinformatics
Data Science
Medical Informatics

Background:

Discovering rare disease datasets is challenging due to fragmented data.
Existing knowledge graphs lack comprehensive patient data integration.
Improving data discoverability is crucial for rare disease research.

Purpose of the Study:

To develop a methodology for enhancing the discoverability of rare disease datasets.
To enrich source data with biological associations.
To create a semantic search interface for patient datasets.

Main Methods:

Extended the Biolink semantic model to include patient data.
Generated a knowledge graph (KG) integrating patient data and biological entities.
Leveraged existing mappings and mapping standards for data integration.

Main Results:

Developed an enriched patient data model.
Created a knowledge graph (KG) with patient data and biological associations.
Enabled a search application with biological context awareness.

Conclusions:

The methodology improves dataset discoverability by enriching data with biological knowledge.
The approach provides a framework for modeling source data to enhance natural language querying.
Techniques illustrated with condition concepts can be applied to other data entities.