The PRETTY (Personalised Prediction of Transplant Toxicity) project aims to develop a personalized prediction model to assess the risk of serious kidney damage following stem cell transplants in leukaemia patients. Its goal is to reduce the risk factors and thereby increase the lifespan and quality of life of those affected.
Currently, some people suffering from depression cannot be helped with standard therapies. Therefore, a national project is investigating how the treatment of depression can be tailored more closely to individual patients.
Systems Medicine Investigation of Alternative Splicing in Cardiac and Renal Diseases.
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.
The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.
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.
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.