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Peer-reviewedCC BY 4.0iMeta

Multi‐omics–driven precision medicine

  • Huibo Li1,2,3
  • Zhe Zhao2
  • Yifan Zhang1
  • Yao Ma4,5
  • Gaofei Hu6
  • Min Zeng7
  • Zhanqun Yang2,4,5,8
  • Zixuan Zhao9
  • Xin Zhou4,5
  • Wei Hu1
  • Yuxuan Sun2,10
  • Meng Su11
9 more authors
  • Jun Li12
  • Matthew Whiteman13
  • Wei Fu4,5
  • Chao Zhong7,14,15
  • Lemin Zheng6
  • Long Chen2,4,5,8
  • Hairong Lv9
  • Rongsheng Zhao1,2
  • Yi Zhun Zhu1,16,17
17 affiliations
  1. 1School of Pharmacy, Faculty of Medicine, Laboratory of Drug Discovery from Natural Resources and Industrialization, Macau University of Science and Technology Macau SAR China
  2. 2Department of Pharmacy Peking University Third Hospital; Peking University Therapeutic Drug Monitoring and Clinical Toxicology Center; Institute for Drug Evaluation, Peking University Health Science Center Beijing China
  3. 3State Key Laboratory of Natural and Biomimetic Drugs, Peking University Beijing China
  4. 4Department of General Surgery Cancer Center, Peking University Third Hospital Beijing China
  5. 5Beijing Key Laboratory for Interdisciplinary Research in Gastrointestinal Oncology, Peking University Third Hospital Beijing China
  6. 6The Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University Beijing China
  7. 7Department of Immunology School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Medicine Innovation Center for Fundamental Researches on Major Immunology‐Related Diseases, Peking University Beijing China
  8. 8Institute of Advanced Clinical Medicine, Peking University Beijing China
  9. 9Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation Tsinghua University Beijing China
  10. 10Department of Pharmacy Children's Hospital, Tianjin University Tianjin China
  11. 11College of Pharmacy, University of Louisiana at Monroe Monroe Louisiana USA
  12. 12Department of Gastroenterology Peking University Third Hospital Beijing China
  13. 13University of Exeter Medical School Exeter UK
  14. 14Department of Rheumatology and Immunology Peking University Third Hospital Beijing China
  15. 15Department of Gastroenterology Peking University First Hospital Beijing China
  16. 16Shanghai Key Laboratory of Bioactive Small Molecules and Department of Pharmacology School of Pharmacy, Fudan University Shanghai China
  17. 17Department of Cardiology, Xiangshan International Medical Center Zhuhai People's Hospital, Macau University of Science and Technology Zhuhai Guangdong China
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Abstract

Precision medicine is increasingly constrained not by a lack of molecular data but by the absence of frameworks that can translate multidimensional biological information into actionable clinical decisions. Multi‐omics‐driven precision medicine (MODPM) addresses this lack by integrating genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome, and clinical context into a multiscale framework that links molecular mechanisms, tissue organization, and patient trajectories. In this review, we propose a conceptual framework for MODPM and examine how advances in multi‐omics technologies, artificial intelligence (AI), and foundation models are reshaping disease modeling, drug development, and precision intervention. We summarize the biological contributions of major omics layers and discuss how AI supports cross‐modal representation learning, contextual modeling, and perturbation‐aware prediction. We highlight drug development as a key translational application of MODPM and further discuss its clinical relevance across three major disease contexts: cancer, autoimmune diseases, and metabolic disorders, including cardiometabolic and renal–metabolic diseases. These examples illustrate how MODPM can support target discovery, disease endotyping, treatment response prediction, and clinical monitoring by analyzing shared mechanisms such as immune dysregulation, metabolic remodeling, chronic inflammation, tissue microenvironmental changes, and gene–environment interactions. Across these settings, MODPM enables finer molecular stratification, the identification of pathway‐dominant disease states, improved response prediction, and dynamic treatment monitoring. We also discuss key barriers to implementation, including data heterogeneity, limited cohort diversity, polygenic complexity, workflow constraints, cost, and ethical issues related to privacy, consent, and data ownership. Overall, the value of MODPM lies not in stacking additional data layers but in building a multiscale, continuously learnable framework to link biological heterogeneity to clinically interpretable and actionable decisions.

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Cite

BibTeXRIS
BibTeX
@article{li2026multiomicsdriven,
  title = {{Multi‐omics–driven precision medicine}},
  author = {Huibo Li and Zhe Zhao and Yifan Zhang and Yao Ma and Gaofei Hu and Min Zeng and Zhanqun Yang and Zixuan Zhao and Xin Zhou and Wei Hu and Yuxuan Sun and Meng Su and Jun Li and Matthew Whiteman and Wei Fu and Chao Zhong and Lemin Zheng and Long Chen and Hairong Lv and Rongsheng Zhao and Yi Zhun Zhu},
  journal = {iMeta},
  year = {2026},
  month = aug,
  doi = {10.1002/imt2.70165},
  url = {https://doi.org/10.1002/imt2.70165},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multi‐omics–driven precision medicine
AU  - Huibo Li
AU  - Zhe Zhao
AU  - Yifan Zhang
AU  - Yao Ma
AU  - Gaofei Hu
AU  - Min Zeng
AU  - Zhanqun Yang
AU  - Zixuan Zhao
AU  - Xin Zhou
AU  - Wei Hu
AU  - Yuxuan Sun
AU  - Meng Su
AU  - Jun Li
AU  - Matthew Whiteman
AU  - Wei Fu
AU  - Chao Zhong
AU  - Lemin Zheng
AU  - Long Chen
AU  - Hairong Lv
AU  - Rongsheng Zhao
AU  - Yi Zhun Zhu
PY  - 2026
DA  - 2026/08/25
JO  - iMeta
DO  - 10.1002/imt2.70165
UR  - https://doi.org/10.1002/imt2.70165
AB  - Precision medicine is increasingly constrained not by a lack of molecular data but by the absence of frameworks that can translate multidimensional biological information into actionable clinical decisions. Multi‐omics‐driven precision medicine (MODPM) addresses this lack by integrating genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome, and clinical context into a multiscale framework that links molecular mechanisms, tissue organization, and patient trajectories. In this review, we propose a conceptual framework for MODPM and examine how advances in multi‐omics technologies, artificial intelligence (AI), and foundation models are reshaping disease modeling, drug development, and precision intervention. We summarize the biological contributions of major omics layers and discuss how AI supports cross‐modal representation learning, contextual modeling, and perturbation‐aware prediction. We highlight drug development as a key translational application of MODPM and further discuss its clinical relevance across three major disease contexts: cancer, autoimmune diseases, and metabolic disorders, including cardiometabolic and renal–metabolic diseases. These examples illustrate how MODPM can support target discovery, disease endotyping, treatment response prediction, and clinical monitoring by analyzing shared mechanisms such as immune dysregulation, metabolic remodeling, chronic inflammation, tissue microenvironmental changes, and gene–environment interactions. Across these settings, MODPM enables finer molecular stratification, the identification of pathway‐dominant disease states, improved response prediction, and dynamic treatment monitoring. We also discuss key barriers to implementation, including data heterogeneity, limited cohort diversity, polygenic complexity, workflow constraints, cost, and ethical issues related to privacy, consent, and data ownership. Overall, the value of MODPM lies not in stacking additional data layers but in building a multiscale, continuously learnable framework to link biological heterogeneity to clinically interpretable and actionable decisions.
SN  - 2770-596X
SN  - 2770-5986
ER  -