Richard Röttger, Associate Professor, Dr. rer. nat.
I am an Associate Professor for Bioinformatics at the University of Southern Denmark (SDU) at the Department for Mathematics and Computer Science (IMADA) and head of the Computational Biology Group. I started at SDU in Summer 2014 as Assistant Professor (promotion to Associate Professor in November 2017) and have since been actively researching in various kinds of machine learning of biomedical data. I furthermore spent 5 month on a Sabbatical leave at the University of Cambridge at the Medical Research Council Laboratory of Molecular Biology (MRC-LMB) in the group of Prof. Madan Babu. Before I joined SDU, I received my PhD (Dr. rer. nat.) from the Max-Planck-Institute for Informatics, in Saarbrücken for my work on Active Transitivity Clustering. I was awarded an International Max Planck Research School (IMPRS) PhD fellowship for my research at the Max Planck Institute. Before my PhD studies, I studied computer science at the Technical University of Munich (TUM) and Technology Management at the Center for Digital Technology and Management (CDTM). During my studies I had two research visits at the University of California at Berkeley at the School of Information as well as at the International Computer Science Institute where my diploma thesis originated. My thesis on the completeness of gene regulatory networks was awarded with the prestigious Siemens price.
Research Interests
The main research interest of my lab lies in the development and establishment of next-generation machine learning and artificial intelligence technology for analyzing big biomedical data and algorithms for systems-medical research.
We are particularly well known for our research on unsupervised learning. One example is ClustEval, a fully automated ensemble clustering platform for massive-scale biomedical data. It is the first bioinformatics tool for de novo predicting the most accurate unsupervised data analysis model and parameter range given a previously unseen data set. Further, we are the core developers of Transitivity Clustering and develop active clustering methods
We also host reference databases of bacterial gene regulatory networks (GRNs) and researched their statistical properties and completeness.
Further, my lab has a history of successful collaborations across faculties. For instance, in collaboration with the Department for Molecular Biology at SDU, we developed techniques to cluster biomolecular networks conjointly with high-dimensional OMICS data. We termed this novel approach “human augmented clustering” and coupled it to time-series network enrichment to identify subnetworks of enriched temporal cross-talk. The software (TiCoNE, very recently published in Systems Medicine) was downloaded already >13,000x times from the Cytoscape app store alone. We further developed tools for de novo discovery of differentially methylated regions in birth-weight discordant monozygotic twins in a collaboration with the Faculty of Health and the Odense University Hospital.
