Summary
This narrative review examines the critical role of longitudinal, multi-layered big biological data in advancing AI and systems biology approaches for precision health. The authors synthesise recent applications of AI in integrating multi-omics datasets, highlight their contributions to digital twin development and novel biomarker discovery, and address the implementation of such data into clinical decision support systems and AI-driven healthcare models.
Regional applicability
The review is international in scope and addresses general methodological frameworks for precision health applicable across healthcare systems, including the United Kingdom National Health Service. However, implementation challenges and data governance requirements will vary by jurisdiction and healthcare infrastructure.
Key measures
Multi-omics data (genomics, proteomics, metabolomics), clinical data, wearable device data, imaging data, dietary information, drug and toxin exposure; biomarkers and drug targets identified through integrated analysis; digital twin parameters
Outcomes reported
The paper reviews applications of AI and systems biology in integrating multi-omics data for characterising whole-body biological functions, digital twin creation, and biomarker discovery. It examines longitudinal multi-omics datasets generated globally and addresses incorporation of big biological data into clinical practice and AI-driven healthcare systems.
Topic tags
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