Our research focuses on learning explainable quantitative models for diagnosis and therapy from high dimensional single-cell omics data.
Clinical Bioinformatics and Machine Learning in Translational Single-Cell Biology
Supervised learning of disease associated cell populations
Health and disease status of multicellular organisms pivotally depends on rare cell populations, such as hematopoietic stem cells or tumor initiating cell subsets. We consider single-cell- and spatially resolved proteomic and transcriptomic readouts, and introduced machine learning approaches to identify – possibly rare – disease associated cell subsets, ultimately enabling comparative single-cell studies, e.g. for immunotherapy response prediction in liver cancer, melanoma or diagnosis of autoimmune disorders and detection spatial tissue determinants of disease association.
Machine learning for structure learning of large dynamic systems in health and disease
Dynamic processes are increasingly studied by high-dimensional single-cell snapshots, complementing efforts based on time-lapse measurements. Such measurements enable us to evaluate cell population distributions and their evolution over time. However, it is not trivial to map these distributions across time and to identify dynamically important cell states. To this end we develop sparse regression, probabilistic graphical modeling, supervised surrogate learning approaches and operator approximation approaches, as well as supervised pseudotime approaches and simulation based trajectory inference for single-cell RNA seq time series analysis, and apply these to reconstruct differentiation processes such as T cell differentiation in cancer and chronic infections.