Computational Systems Medicine

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

News

Tim Kacprowski has been invited to give a talk at the 2nd International Congress on Precision Medicine.

Olga Tsoy will join as a Post-Doc. Amit Fenn and Gihanna Galindez join as PhD Students. Chit Tong Lio starts her Master’s thesis. …

Members

Avatar

Judith Bernett

BSc Student

Avatar

Tim Faro

BSc Student

Avatar

Amit Fenn

PhD Student

Avatar

Gihanna Galindez

PhD Student

Avatar

Tim Kacprowski

Group Leader

Avatar

Christoph Kloppert

BSc Student

Avatar

Manuela Lautizi

PhD Student

Avatar

Olga Lesina

Student Research Assistant

Avatar

Nils Mehrtens

BSc Student

Avatar

Pauline Nickel

BSc Student

Avatar

Fanny Rößler

BSc Student

Avatar

Sepideh Sadegh

PhD Student

Avatar

Jonas Schäfer

Student Research Assistant

Avatar

Andreas Stelzer

BSc Student

Avatar

Chit Tong Lio

MSc Student

Avatar

Olga Tsoy

Post-Doc

Alumni

Avatar

Chris Li

PREP Student

Avatar

Weilong Li

PhD Student

Avatar

Niklas Probul

BSc Student

Avatar

Lorenzo Viola

MSc Student

Projects

REPO-TRIAL

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.

FeatureCloud

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

Network-Based Epistasis Detection

We tackle the challenge of higher-order epistasis detection using biological networks to narrow the search space and GPU computing to improve the efficiency. Phenotype-specific epistasis-modules extracted from larger networks will help to better understand the underlying biological mechanisms of different phenotypes.

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.

Software & Ressources

Coming soon