Project title: Tools to translate proteomes to human health and diagnostics

Development of robust, versatile and easy-to-use computational tools is crucial to translate the emerging proteome data to patient benefits. Despite advances in proteomics measurement technologies, a common problem in most protein biomarker studies remains that the findings cannot be validated in new studies. This project addresses this challenge by developing a robust computational framework for proteome data that uses longitudinal follow-up data over time and is widely applicable on different types of proteome data, including the emerging fields of single cell proteomics and metaproteomics. By combining multidisciplinary expertise in statistical and machine learning methods, proteomics technologies and clinical research, the project is anticipated to accelerate the development of improved diagnosis, prognosis and treatment strategies for complex diseases, such as diabetes and cardiovascular diseases. The computational framework will be useful in a wide range of medical applications.