Computational Systems Medicine

Research Group @ Chair of Experimental Bioinformatics @ TUM
Our group aims to elucidate the molecular mechanisms behind phenotypes in general and human diseases, in particular. To that end, we develop integrative bioinformatics methods leveraging network analysis, machine learning techniques, and statistical approaches. We apply own and existing approaches in close collaboration with biologists and physicians to derive insights from multi-omics data.


Tim Kacprowski has been invited to give a talk at the 9th Gene Quantification Event qPCR dPCR & NGS 2019.

PhD student position for the devlopment of graph-based machine learning techniques to identify disease- and patient-specific …


De novo network enrichment

We develop tools that leverage information from molecular interaction networks in understanding molecular profiling data. De novo network enrichment tools extract subnetworks that mechanistically explain a phenotype of interest, e.g. a disease.


The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.


The EU H2020 project REPO-TRIAL aims at developing an in silico approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrate heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome) and develop graph-based machine learning approaches to investigate this highly complex data.

Recent Publications

More Publications

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. …

Changes in intestinal microbiome composition are associated with inflammatory, metabolic, and malignant disorders. We studied how …

OMICs subsume different physiological layers including the genome, transcriptome, proteome and metabolome. Recent advances in …

Software & Resources

Coming soon