Biomedical Data Science
About the Biomedical Data Science group
The research of the Biomedical Data Science group is centered on the development and application of software tools to identify diagnostic biomarker signatures, characterize potential molecular drug targets and screen drug-like molecules. Our applications focus on the integrated statistical analysis of large-scale biomolecular and clinical data for complex diseases, primarily for the neurodegenerative disorders Parkinson’s and Alzheimer’s disease. In particular, we develop machine learning methods for the joint analysis of different types of omics data from patient and control subjects by exploiting prior knowledge from cellular pathways, molecular networks and clinical data. Integrated analyses of these information sources have the potential to provide models with increased robustness for diagnostic specimen classification and patient subgroup stratification. To elucidate specific molecular disease mechanisms, we complement these statistical investigations by bioinformatics approaches for cross-species comparisons, the study of gender-specific pathological alterations, and aging-related biomolecular changes as disease-promoting factors. Putative biomarker signatures and targets derived from our computational approaches, e.g. developed as part of the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD), are then examined and validated in collaboration with experimental groups at the LCSB and external partners.