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<p>Multi-omics promises to transform medicine by providing holistic disease insights through interacting molecular layers, involving DNA, RNA, proteins and metabolites. The underlying technologies have matured rapidly, currently enabling higher throughputs at lower costs. Yet as multi-omics moves from research to routine care, the central challenge is no longer data generation, but standardizing and interpreting complexity within health systems built for discrete tests. In this Perspective, we chart the path from assay to implementation by demonstrating how integrative analyses outperform single modalities, as well as by emphasizing that multiplexing, high dimensionality and probabilistic interpretation introduce risks to reproducibility and clinical validity. We examine computational strategies for multimodal integration, highlighting the importance of explainable AI for auditability and regulatory trust. Drawing on lessons from early national programs, we suggest that scalable clinical adoption depends on interoperable digital infrastructures, harmonized quality standards and multidisciplinary care models that embed multi-omics into everyday practice.</p>

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Mapping the path to clinical implementation of multi-omics

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Said I. Ismail,
  • Chadi Saad,
  • Mohamed A. Elrayess,
  • Ahmad A. L. khleifat,
  • Nagham Nafiz Hendi,
  • Yasser AlSarraj,
  • Radja Messai Badji,
  • Wadha A. A. L. Muftah

摘要

Multi-omics promises to transform medicine by providing holistic disease insights through interacting molecular layers, involving DNA, RNA, proteins and metabolites. The underlying technologies have matured rapidly, currently enabling higher throughputs at lower costs. Yet as multi-omics moves from research to routine care, the central challenge is no longer data generation, but standardizing and interpreting complexity within health systems built for discrete tests. In this Perspective, we chart the path from assay to implementation by demonstrating how integrative analyses outperform single modalities, as well as by emphasizing that multiplexing, high dimensionality and probabilistic interpretation introduce risks to reproducibility and clinical validity. We examine computational strategies for multimodal integration, highlighting the importance of explainable AI for auditability and regulatory trust. Drawing on lessons from early national programs, we suggest that scalable clinical adoption depends on interoperable digital infrastructures, harmonized quality standards and multidisciplinary care models that embed multi-omics into everyday practice.