What we do
The primary research interest of our group lies at the intersection of machine learning and medicine. We work on advancing and developing novel machine learning techniques for precision medicine, the life sciences and clinical data analysis. The field of action comprises many areas such as prediction of response to treatment in personalized medicine, (sparse) biomarker detection, tumor classification or the understanding of interactions between genes or groups of genes. We approach these challenges data and problem driven. In close collaboration with physicians we identify gaps where current technologies fail and develop tailored solutions.
Find out about the Group’s activities
Main publications 2019
- Daunhawer I et al.
Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning
Pediatric Research, http://dx.doi.org/10.1038/s41390-019-0384-x - Prabhakaran S et al.
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps
Artificial Intelligence in Medicine, http://dx.doi.org/10.1007/978-3-030-21642-9_35 - Goulooze S et al.
Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap
Clinical Pharmacology and Therapeuthics, http://dx.doi.org/10.1002/cpt.1744
