What we investigate

Our research centers around machine learning in translational single-cell biology and addresses challenges of learning explainable quantitative models from high dimensional single-cell omics data in the context of cancer and immune biology.

translational single-cell biology, machine learning

Our research in more detail
Prof. Manfred Claassen

Prof. Manfred Claassen
Universitätsklinikum Tübingen
Clinical Bioinformatics
Otfried-Müller-Straße 10
DE-72076 Tübingen, Germany

Email   Website

Selected publications

SKINTEGRITY.CH Principal Investigators are in bold:

  • Wietecha MS, Lauenstein D, Cangkrama M, Seiler S, Jin J, Goppelt A, Claassen M, Levesque MP, Dummer R, Werner S (2023). Phase-specific signatures of wound fibroblasts and matrix patterns define cancer-associated fibroblast subtypes. Matrix Biol, S0945-053X(23). Epub ahead of print.
  • Gupta R, Cerletti D, Gut G, Oxenius A, Claassen M. Simulation-based inference of differentiation trajectories from RNA velocity fields. Cell Rep Methods, 2(12), 100359.
  • He Y, Tacconi C, Dieterich LC, Kim J, Restivo G, Gousopoulos E, Lindenblatt N, Levesque MP, Claassen M and Detmar M (2022). Novel blood vascular endothelial subtype-specific markers in human skin unearthed by single-cell transcriptomic profiling. Cells, 11(7), 1111.
  • Otesteanu CF, Ugrinic M, Holzner G, Chang YT, Fassnacht C, Guenova E, Stavrakis S, deMello A and Claassen M. A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics. Cell Rep Methods, 1(6), 100094.