Advances in multi-omics technologies, cohort study design, data science, and machine learning are transforming evidence-based medicine, offering a promising outlook for the future of next-generation "deep" medicine. We hereby summarized the development trends in multi-omics experimental techniques, including genomics and epigenomics sequencing, transcriptomics and single-cell transcriptomics, proteomics, metabolomics, microbiomics, imaging, and biosensors. Furthermore, we introduced progress in big data analysis methods such as genome-wide association studies, interpretation of genome-wide association signals, polygenic risk scoring, Mendelian randomization, and artificial intelligence algorithms. Additionally, we discussed the clinical applications of these technologies in disease subtyping, diagnosis and prediction, drug development, and clinical trial design. Finally, we discussed the challenges faced and explored future directions in cohort study design, data management and sharing, and the enhancement of international collaboration.

multi-omics  /  big data  /  medical research  /  clinical applications