Systems Medicine

Research Lead

General aim

Systems medicine studies the complex interactions between genes, proteins, and metabolites as well as the microbiome to understand how they contribute to disease mechanisms, symptoms and prognosis. It aims to uncover the underlying disease mechanisms by taking a systemic perspective and considering all interconnected molecular and clinical factors. Due to the vast amount and heterogeneity of data, which is typically collected in systems medicine studies, high-dimensional statistics and state-of-the-art artificial intelligence methods are indispensable for successful data analysis. The long-term goal is to translate systems-level understanding of disease into clinical practice for example, by identifying novel biomarkers, supporting personalised therapies or predicting disease trajectories.

Focus of research

The Clinical Data Science Group aims at a holistic integration of different omics data layers by state-of-the-art artificial intelligence algorithms to improve our understanding of systemic diseases, their diagnosis as well as prognosis. To this end, we apply, for example, data-driven network inference algorithms to reconstruct biological regulatory networks from (multi-)omics data. Furthermore, we are interested in the implementation of multi-omics data analysis tools into easy-to-use pipelines and open-source platforms.