Precision medicine aims to tailor disease prevention, diagnosis, and treatment to individual biological differences. Despite decades of progress in genomics, the static nature of genetic information limits its ability to capture the dynamic biological processes underlying human health and disease. Proteins, the direct executors of cellular functions, provide a more immediate and functional view of human health.
In a recent review, researchers from Fudan University summarize how high-throughput proteomics is rapidly emerging as a powerful tool to advance precision medicine. Modern proteomic technologies now allow scientists to simultaneously quantify thousands of proteins in biological samples such as blood, cerebrospinal fluid, and tissues, providing a comprehensive snapshot of physiological and pathological states.
These large-scale proteomic datasets are already driving major breakthroughs in biomarker discovery. By profiling protein changes associated with disease onset and progression, researchers have identified biomarkers capable of predicting disease risk years before symptoms appear. Such discoveries may enable earlier diagnosis, improve patient stratification and more personalized clinical interventions.
Beyond diagnostics, proteomics is also reshaping therapeutic development. Because proteins represent the majority of drug targets, proteomic technologies provide critical insights into disease mechanisms and drug–target interactions. Integrating proteomic data with genetic information and clinical records allows researchers to identify promising therapeutic targets and evaluate drug responses more precisely.
The review also highlights the growing role of artificial intelligence in proteomics research. Machine learning and deep learning algorithms can identify meaningful patterns from complex, high-dimensional proteomic datasets, improving disease prediction models and facilitating multi-omics data integration. In addition, advances in protein structure prediction, drug–target interaction (DTI) prediction, and computational molecular design are helping researchers better understand protein function, identify potential therapeutic targets, and accelerate the development of new drug candidates.
Despite these advances, several challenges remain before proteomics can be widely implemented in clinical practice. These include standardizing experimental workflows, improving cross-platform data consistency, and establishing large, diverse population datasets. Continued technological innovation and international collaboration will be essential to overcome these barriers.
Looking ahead, the convergence of high-throughput proteomics, artificial intelligence, and large population cohorts is expected to further transform biomedical research. Together, these advances may enable more accurate disease prediction, earlier diagnosis, and more effective personalized treatments, ultimately accelerating the realization of precision medicine.
Science Bulletin
Systematic review