Data drives most of his statistical omics research: to provide a generic, robust solution for a given study, and one likely solves similar problems for many studies. His research interests cover a wide spectrum, including differential expression (multiple) testing, network estimation and omics integration modeling. His main fascination nowadays is omics-based clinical prediction and classification, by either statistical or machine learners. Here, he focusses on developing methods to improve predictive performance and biomarker selection by structural use of complementary data (co-data), e.g. from external studies or data bases. They directly apply and test such methods in a number of collaborative projects on cancer diagnostics and prognostics.
The environment we live in has a dominant impact on our health. It explains an estimated seventy percent of the chronic disease burden. Where we live, what we eat, how much we exercise, the air we breathe and whom we associate with; all of these environmental factors play a role. The combination of these factors over the life course is called the exposome. There is general (scientific) consensus that understanding more about the exposome will help explain the current burden of disease and that it provides entry points for prevention and ...Read More