CPN-Map

Computational Precision Nutrition

Group Lead

General aim

The aim of our group is to understand and predict individual metabolic responses to nutrition by integrating dietary, microbiome, and metabolome data. Using explainable and causal artificial intelligence, we analyze large multi-omics cohorts to uncover how diet influences health trajectories and to support personalized dietary recommendations for disease prevention and care.

Focus of research

Our research focuses on computational models of diet–microbiome–metabolome interactions and their implications for metabolic and neurodegenerative diseases. We develop and validate AI-driven approaches for the integration and causal interpretation of high-dimensional multi-omics data. Special emphasis is placed on explainability, reproducibility, and the transfer of AI models into biomedical research and clinical nutrition contexts. In cooperation with national and international partners, we aim to establish digital infrastructures for sustainable, FAIR-compliant use of nutrition-related health data and to advance precision nutrition as a data science discipline.