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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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MetagenomicKG: a knowledge graph for metagenomic applications
Chunyu Ma1, Shaopeng Liu1, David Koslicki1,2,3,4
1Huck Institutes of the Life Sciences, Pennsylvania State University, State College, Pennsylvania, USA.
Biorxiv : the Preprint Server for Biology
|April 1, 2024
Summary
MetagenomicKG unifies microbial genomic data from multiple databases into a knowledge graph. This enables new insights into microbe-disease relationships and improves pathogen prediction.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Metagenomic studies analyze vast microbial genomic data, relying on diverse databases like GTDB, KEGG, and BV-BRC for annotation.
- Inconsistent nomenclature across these databases hinders data integration, representation, and utilization.
- Knowledge graphs (KGs) offer a solution for organizing complex biological entities and their relationships, but their application in metagenomics is limited.
Conclusions:
- MetagenomicKG addresses the challenge of data heterogeneity in metagenomics.
- This approach enhances biological understanding by revealing hidden patterns and relationships.
- The developed KG supports advanced applications in microbial community analysis and disease research.
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