Our group is studying metabolic diseases (e.g. type 2 diabetes and complications). We use statistical, bioinformatical and machine learning approaches to integrate metabolomics, transcriptomics, proteomics, epigenomics, and genomics data, as well as information from public databases to better understand the pathophysiological mechanisms. This helps us to identify candidate biomarkers and drug targets, with the final goal to translate our discoveries into clinical practice.
Integration of proteomic and metabolomics data to study chronic kidney disease (CKD) in hyperglycemia
The successful candidate will work independently on a project to integrate OMICs data to study CKD in hyperglycemia. Hyperglycemia exerts detrimental effects on the kidney and is one of the leading causes of CKD. However, the onset of CKD can be partly prevented or at least delayed by increased physical activity in individuals with hyperglycemia. The aim of the project is to analyze large-scale molecular data from a population-based human cohort, and to identify candidate biomarkers of CKD and thus enabling its detection. Early detection of CKD in hyperglycemic individuals is essential for the development of personalized strategies to prevent and / or treat CKD.