Prof. dr. ir. Marcel Reinders
Professor, Head of the Pattern Recognition & Bioinformatics Group
Marcel J.T. Reinders received his MSc degree in Applied Physics and a PhD degree in Elec¬trical Engineering from Delft University of Technology, The Netherlands, in 1990 and 1995, respectively. In 2005, he became a Professor in Bioinformatics within the Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology in which he now heads the ‘Pattern Recognition and Bioinformatics’ section. In 2010 he became one of the scientific directors of the Netherlands Bioinformatics Centre (NBIC). The background of Marcel Reinders is within pattern recognition. Besides studying fundamental issues, he applies pattern recognition techniques to the areas of bioinformatics, computer vision and context-aware recommender systems. His special interest goes towards understanding complex systems (such as biological systems) that are severely under-sampled. He (co-)authored more than 200 scientific papers of which more than 75 in peer-reviewed journals.
Dr. ir. Jeroen de Ridder
Assistant professor, lab coordinator
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