Multimodal Approaches for Translational Medicine

Multimodal Approaches for Translational Medicine (TRANSMED) brings together research groups that apply and integrate diverse measurement technologies and AI-based methods in the study of metabolic, cardiovascular, and oncological diseases to improve understanding of the transition from health to disease.

These diseases account for over 50% of all global deaths each year, and their share is expected to increase due to population ageing and changes in lifestyle.

Theme 1: Biological mechanisms at single-cell resolution. TRANSMED promotes the broad adoption of emerging technologies by combining state-of-the-art omics and imaging technologies to study biological mechanisms.

Theme 2: Tools for data integration. We develop novel methods to (i) detect pathophysiological changes in time-series data in order to predict disease risk and progression, (ii) advance image analysis towards spatially and temporally resolved quantitative information on different cellular mechanisms for the identification and prioritization of precision medicine targets, and (iii) create AI-based risk profiling tools to support personalized treatment planning.

Theme 3: Clinical and population-based data. UEF hosts several world-leading cohort and intervention studies, which we integrate with local and national healthcare registers to develop new approaches for the prediction, prevention, and treatment of cardiometabolic diseases and cancer.