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

Giovanni Maria De Filippis¹, Domenico Amalfitano¹, Cristiano Russo¹

¹Department of Electrical Engineering and Information Technology DIETI, University of Naples Federico II, Via Claudio, 21, Naples, 80125, Italy.

Journal of Biomedical Informatics

|March 28, 2025

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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.

Keywords:

Integrative bioinformatics Knowledge graphs Multi-omic integration Ontologies Semantic technologies Systematic mapping study

Related Experiment Videos

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.