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.
Exposome-NL is a Dutch consortium of over fifty scientists from different disciplines, universities and medical centres. Together the scientists will systematically sequence the environmental factors influencing our health.Read More