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Updated: Jun 28, 2025
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
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An open source knowledge graph ecosystem for the life sciences
Tiffany J Callahan1,2, Ignacio J Tripodi3, Adrianne L Stefanski4
1Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. tiffany.callahan@cuanschutz.edu.
Scientific Data
|April 11, 2024
Summary
PheKnowLator automates the FAIR construction of phenotype knowledge graphs (KGs) with customizable representations. This semantic ecosystem addresses biomedical data integration challenges, offering flexibility without sacrificing performance or usability.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Data Science
Background:
- Translational research necessitates integrating biological data across multiple scales.
- Advances in multi-omics and sequencing generate vast datasets, posing significant integration challenges.
- Existing knowledge graph (KG) construction methods offer limited flexibility in knowledge representation for complex biomedical problems.
Purpose of the Study:
- To introduce PheKnowLator, a semantic ecosystem for automated, FAIR (Findable, Accessible, Interoperable, Reusable) KG construction.
- To provide a fully customizable knowledge representation framework for biomedical KGs.
- To address limitations in existing KG construction tools regarding flexibility and choice.
Main Methods:
- Developed PheKnowLator as a semantic ecosystem with KG construction resources, analysis tools, and benchmarks.
- Included data preparation APIs, SPARQL endpoint resources, and abstraction algorithms.
- Designed for fully customizable knowledge representation in ontologically grounded KGs.
Main Results:
- Evaluated PheKnowLator against existing open-source KG construction methods.
- Analyzed computational performance using 12 large-scale KG construction tasks.
- Demonstrated that PheKnowLator enables fully customizable KGs with maintained performance and usability.
Conclusions:
- PheKnowLator offers a flexible and automated approach to constructing FAIR biomedical knowledge graphs.
- The ecosystem supports customizable knowledge representation, overcoming limitations of existing tools.
- PheKnowLator enhances the integration of multi-omics data for translational research.
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