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Integrating multi-omics data for personalized nutrition using knowledge graphs and graph neural networks: A
Sharath Thoniyot1, Vijayakumar Balakrishnan1
1Department of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai, Campus, Dubai International Academic City, Dubai, United Arab Emirates.
Computational Biology and Chemistry
|November 15, 2025
Summary
Knowledge graphs and Graph Neural Networks (GNNs) integrate multi-omics data for personalized nutrition. This approach enhances dietary plan customization and individual health outcome optimization.
Area of Science:
- Biomedical Informatics
- Nutritional Science
- Computational Biology
Background:
- Personalized nutrition requires integrating diverse biological data (genomics, proteomics, metabolomics, microbiome).
- Existing methods struggle to capture complex inter-omics relationships for tailored dietary recommendations.
- Knowledge graphs and Graph Neural Networks (GNNs) offer a novel framework for multi-omics data integration.
Purpose of the Study:
- To review the application of knowledge graphs and GNNs in personalized nutrition.
- To assess the effectiveness of graph-based approaches for multi-omics data integration.
- To highlight advancements and challenges in using these technologies for customized dietary interventions.
Main Methods:
- Systematic literature review of scientific publications from 2015-2025.
- Analysis of knowledge graph construction for multi-relational biological data.
- Evaluation of GNNs for analyzing graph-based multi-omics information.
Main Results:
- Knowledge graphs provide a scalable model for biological information representation.
- GNNs identify complex inter-omics relationships missed by traditional statistics.
- Graph-based methods demonstrate superior capability in capturing biological complexity compared to traditional approaches.
Conclusions:
- Knowledge graphs and GNNs enable a paradigm shift towards precision nutrition.
- These technologies improve individual response prediction, biomarker discovery, and nutritional therapy development.
- Addressing data quality, model scalability, interpretability, and ethical concerns is crucial for future advancements.
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