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Updated: Sep 26, 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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Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories
Alexander M Waldrop1, John B Cheadle2, Kira Bradford3
1Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA.
Bioinformatics (Oxford, England)
|April 20, 2022
Summary
Dug is a new semantic search tool that helps researchers find relevant biomedical datasets. It uses knowledge graphs to improve search accuracy and explainability, outperforming traditional search methods.
Area of Science:
- Biomedical Informatics
- Data Science
- Bioinformatics
Background:
- The proliferation of public data resources necessitates advanced tools for identifying relevant datasets.
- Navigating heterogeneous data repositories poses a significant challenge for scientific inquiry.
Purpose of the Study:
- To develop Dug, a semantic search tool for biomedical datasets.
- To improve the discoverability of relevant datasets by utilizing evidence-based relationships from knowledge graphs.
Main Methods:
- Dug was developed within the NHLBI BioData Catalyst ecosystem.
- Indexed over 15,911 study variables from public datasets.
- Utilizes curated knowledge graphs for evidence-based relationship identification.
Main Results:
- Dug achieved a total recall of 0.79, outperforming Elasticsearch's 0.76 on a curated dataset.
- Dug significantly improved total recall (0.36 vs. 0.14) when using synonyms or related concepts, with no significant loss in precision.
Conclusions:
- Dug enhances the semantic search capabilities for biomedical datasets.
- The tool offers improved accuracy and explainability in dataset retrieval.
- Dug is freely available, promoting wider adoption in the research community.
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