Welcome to the Computational Biology Lab!
In theory, there is no difference between theory and practice. But, in practice, there is.
-- Jan L.A. van de Snepscheut
We are living in an exciting time that sees a change in the way biology and medicine is conducted. One major reason for this change is due to technological progress that has been driven by the human genome project. As a result, we are nowadays in a position to measure unimaginable amounts of molecular and cellular data. Our group is embarking on the journey to make sense of these data.
Currently, we are especially interested in the analysis of gene expression data from microarray experiments on a pathways level and sequencing data. In close collaboration with groups from Biology and Medicine we are studying cancer related questions. An ultimate goal of our research is to contribute to the deciphering of regulatory networks with respect to their reconstruction and functional analysis to shed light on causal mechanisms underlying complex diseases and pathological phenotypes.
Our methodological research aims to develop and improve computational, statistical and mathematical methods in Bayesian statistics, exploratory data analysis, graph theory, machine learning, Monte Carlo methods, multivariate analysis, optimization and statistical inference to apply them to problems in Systems Medicine.
- Computational Genomics
- Exploratory Data Analysis & Visualization
- Machine Learning & Statistics
- Network Medicine
- Personalized Medicine
- c3net: Infering large-scale gene networks with the C3NET inference algorithm - R package. Available from the CRAN repository.
- bc3net: Bagging Statistical Network Inference from Large-Scale Gene Expression Data - R package. Available from the CRAN repository.
- BClymphomaGRN: Gene regulatory network for B Cell lymphoma - R package. Available from the CRAN repository.