Biomedical Data Science

The Biomedical Data Science group at the LCSB.
Left to right: Quentin Klopfenstein, Muhammad Ali, Enrico Glaab, Elisa Gomez de Lope, Leon-Charles Tranchevent, Rebecca Ting Jiin Loo, Armin Rauschenberger

About the Biomedical Data Science group

The Biomedical Data Science group works on the development and application of software tools to identify diagnostic biomarker signatures, investigate pathway and network activity alterations in omics data for drug target prioritisation, and screen drug-like molecules for selected targets. Our applications focus on the analysis of omics and clinical data for neurodegenerative disorders, primarily for Parkinson’s (PD) and Alzheimer’s (AD) disease. In particular, we develop integrative 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. Joint analyses of these complementary information sources have the potential to provide models with increased robustness for diagnostic specimen classification and patient subgroup stratification. To obtain more specific models of disease mechanisms, we also investigate disease-related gender differences and apply cross-species analyses. Putative marker signatures and targets derived from our computational approaches are then investigated and validated in collaboration with experimental groups at the LCSB and in external partner institutes.