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The rapid advancement of high-throughput technologies has led to the generation of vast amounts of omics data, including genomics, epigenomics, and metabolomics. Integrating these diverse datasets has become essential for gaining comprehensive insights into complex biological systems and enhancing personalized healthcare solutions. This critical review examines the current state of multi-omics data integration platforms, highlighting both the strengths and limitations of existing tools. By evaluating the latest digital platforms, such as GraphOmics, OmicsAnalyst, and others, the paper explores how they support seamless integration and analysis of omics data in healthcare applications. Special attention is given to their role in clinical decision-making, disease prediction, and personalized medicine, with a focus on their interoperability, scalability, and usability. The review also discusses the challenges these platforms face, such as data complexity, standardization issues, and the need for improved machine learning and AI-based analytics. Finally, the paper proposes directions for future research and development, emphasizing the importance of more advanced, user-friendly, and secure platforms that can better serve comprehensive healthcare needs.
Papadaki et al. (Tue,) studied this question.