A systematic mapping study of semantic technologies in multi-omics data integration

Objective: The integration of multi-omics data is essential for understanding complex biological systems, providing insights beyond single-omics approaches. However, challenges related to data heterogeneity, standardization, and computational scalability persist. This study explores the interdisciplinary application of semantic technologies to enhance data integration, standardization, and analysis in multi-omics research. Methods: We performed a systematic mapping study assessing literature from 2014 to 2024, focusing on the utilization of ontologies, knowledge graphs, and graph-based methods for multi-omics integration. Results: Our findings indicate a growing number of publications in this field, predominantly appearing in high-impact journals. The deployment of semantic technologies has notably improved data visualization, querying, and management, thus enhancing gene and pathway discovery, and providing deeper disease insights and more accurate predictive modeling. Conclusion: The study underscores the significance of semantic technologies in overcoming multi-omics integration challenges. Future research should focus on integrating diverse data types, developing advanced computational tools, and incorporating AI and machine learning to foster personalized medicine applications.