We are working within the intersection of data sciences/data analytics and life sciences. In modern biomedical research more and more large scale and highly complex datasets are generated. Examples are -omics data (genomics, transcriptomics, methylomics, proteomics, ...), which are characterized by a very high number of molecular features coupled with a comparably low number of samples. A key question is, how to robustly identify biological mechanisms in these data and to develop robust and predictive models.

The focus of our research lies on development and application of algorithms for analyzing large scale biological data (e.g. -omics data) of different type. The current research specifically concentrates:

  • statistical learning methods in personalized/precision medicine (e.g. biomarker signatures)
  • structure learning of molecular interaction networks
  • modeling of molecular networks