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MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping
Saul Langarica1,2, Young-Tak Kim2, Adham Alkhadrawi2
1Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
Briefings in Bioinformatics
|September 23, 2025
Summary
MINERVA is a new platform mapping microbe-disease links from scientific literature using a large language model. It helps researchers uncover hidden associations for better diagnostics and treatments.
Area of Science:
- Microbiology and Bioinformatics
- Computational Biology
- Medical Informatics
Background:
- Bacterial pathogens cause significant global disease burden.
- Understanding microbe-human health interactions is crucial for medical advancements.
- Scattered microbiome research data hinders comprehensive analysis and clinical application.
Purpose of the Study:
- To introduce MINERVA, an innovative platform for mapping microbe-disease associations.
- To address the challenge of fragmented knowledge in microbiome research.
- To facilitate the translation of microbiome research into clinical practice.
Main Methods:
- Leveraging a fine-tuned large language model to process scientific literature.
- Constructing an ontology-driven knowledge graph of microbe-disease associations.
- Developing specialized modules for analysis, visualization, and personalized microbiome composition.
Main Results:
- Systematic mapping of microbe-disease associations across extensive literature.
- Creation of a transparent and accurate knowledge graph for efficient exploration.
- Identification of hidden connections and actionable insights through advanced algorithms.
Conclusions:
- MINERVA bridges the gap between microbiome research and clinical applications.
- The platform accelerates the discovery of microbe-disease interactions.
- MINERVA has the potential to transform patient care and advance medical science.
