Our interdisciplinary group is headed by Prof. Dr. Dominik Heider. The main focus of our research is set on the development of computational solutions from the field of Machine Learning for solving biomedical problems, e.g., algorithms for predicting drug resistance of pathogens or for modeling of diseases. Thereby addressing questions of how to embed and integrate multi-modal medical data, how to make the prediction models explainable, how to derive causal relationships, and how to make these models privacy-preserving.

In another main part of our research, we aim to develop new methods and algorithms for analyzing omics data, e.g., (meta-)genomics and (meta-)transcriptomics or gene expression, as well as genome assembly and functional annotation. 

Head of the working group
Prof. Dr. Dominik Heider