My work is carried out, partly at the Netherlands Cancer Institute in Amsterdam (in the Wessels group), and partly at the Delft University of Technology in Delft (in the Reinders group). I am researching new statistical and machine learning frameworks [1, 2] to analyze retroviral insertional mutagenesis data that should lead to the discovery new cancer genes  and cancer pathways. The current focus is on integrating these methods and data with other data types such as gene expression data.
Some key publications of my work include:
 de Ridder, et al. Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLoS Comput Biol, 2006, 2, e166
 de Ridder, J, et al. Co-occurrence analysis of insertional mutagenesis data reveals cooperating oncogenes. Bioinformatics, 2007, 23(13), i133-i141
 Uren GA, Kool J, et al. Large-scale mutagenesis in p19(ARF)- and p53-deficient mice identifies cancer genes and their collaborative networks. Cell. 2008, May 16;133(4):727-41