A systematic mapping study of semantic technologies in multi-omics data integration
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Summary
This summary is machine-generated.Semantic technologies enhance multi-omics data integration, overcoming challenges in heterogeneity and scalability. This approach improves data analysis, leading to better gene discovery and disease insights.
Area Of Science
- Bioinformatics
- Computational Biology
- Genomics
Background
- Multi-omics data integration is crucial for understanding complex biological systems.
- Challenges include data heterogeneity, standardization, and computational scalability.
- Semantic technologies offer a novel approach to address these integration issues.
Purpose Of The Study
- To explore the application of semantic technologies for multi-omics data integration.
- To assess the impact of ontologies, knowledge graphs, and graph-based methods.
- To enhance data standardization, analysis, and discovery in multi-omics research.
Main Methods
- A systematic mapping study of literature from 2014 to 2024.
- Focus on research utilizing semantic technologies for multi-omics integration.
- Analysis of trends and applications of ontologies and knowledge graphs.
Main Results
- A significant increase in publications on semantic technologies for multi-omics integration.
- Demonstrated improvements in data visualization, querying, and management.
- Enhanced gene and pathway discovery, deeper disease insights, and improved predictive modeling.
Conclusions
- Semantic technologies are vital for overcoming multi-omics integration challenges.
- Future work should focus on integrating diverse data types and developing advanced computational tools.
- Integration of AI and machine learning can foster personalized medicine applications.