PI's: Prof. Dr. Alfons Kemper, Prof. Dr. Klaus A. Kuhn    
PhD Students: Fabian Prasser, Dipl.-Inf.; Sebastian Wurst, Dipl. Inf.; Gregor Lamla, Dipl.-Inf.

After the success of the Human Genome Project, revolutionary opportunities for medicine have become available through the utilization of genetic information in health care. Advances in molecular life sciences, biomedical sciences and engineering have significantly influenced diagnostic and therapeutic options. The etiology of human diseases and their progression is often reflected by unexpected patterns of evidence at the molecular level. Molecular mechanisms of disease are being understood better than ever before, and disease patterns can be understood with increasing granularity down to the level of molecules. With increasing amounts of human genomic information at hand, the challenge is to correlate medical phenotypic information with its genomic and epigenetic counterparts.

Personalized medicine is focusing on tailoring treatment to the genetic profile of an individual patient by using information from the patient's genotype and by correlating it with medical information. Research topics are the discovery of markers for diagnostic purposes, the correlation of markers with the clinical course of disease, the development of new drugs, and a deeper understanding of the causation of disease. Population-based studies are about to correlate genetic variation with clinically defined phenotypes taking into account environmental factors. New fine-grained and translational insights into disease mechanisms can be used for personalized medicine, including identifying personal risks and targeting preventive interventions (behavioral, social and medical), and to gain and apply knowledge about genetic variation in the population.

These developments have made information integration essential. Moreover, translating results from basic research (bench) into medical care (bedside) has massively gained in importance. In order to support and accelerate the translational cycle from genomic, molecular, and clinical data collection to individualized and tailored treatment, including new types of clinical studies, evaluation, and the generation of new hypotheses, highly distributed and heterogeneous data sources and data types need to be integrated.