While genomic and clinical data, as provided by most recent techological advances, carry a wealth of information, their analysis pose major, both theoretical and practical challenges. Our research group focuses on such challenges and develops novel computational and mathematical approaches to overcome the related issues.
Experimental and in particular clinical data is often noisy and comes with clear statistical biases. Therefore a particular expertise of ours is on statistical modeling and learning as theoretical frameworks. Applications of our current greatest interest are to detect genomic variants in next-generation sequenced genomes and the prediction and classification of disease (sub-)types by integrated analysis of most recent genomic and transcriptomic data. Thereby we particularly focus on all aspects of cancer-related issues.