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Clinical Bioinformatics and Machine Learning in Translational Single-Cell Biology

Clinical Bioinformatics and
Machine Learning in Translational Single-Cell Biology

Our research focuses on learning explainable quantitative models for diagnosis and therapy from high dimensional single-cell omics data.

Supervised learning of disease associated cell populations

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

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.

Selected publications

Selected publications

  • Simulation based inference of differentiation trajectories from RNA velocity fields.

    Gupta R, Cerletti D, Gut G, Oxenius A, Claassen M*

    Cell Reports Methods, accepted, 2022.

  • A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

    Otesteanu CF, Ugrinic M, Holzner G, Chang YT, Fassnacht C, Guenova E, Stavrakis S, deMello A, Claassen M*

    Cell Reports Methods, 1(6), 100094, 2021

  • Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data

    Kopf A, Fortuin V, Somnath VR, Claassen M*

    PLoS Computational Biology, 17 (6), e1009086, 2021

  • Landscape of exhausted virus-specific CD8 T cells in chronic LCMV infection.

    Sandu I, Cerletti D, Borsa M, Spadafora I, Welten SPM, Stolz U, Oxenius A*, and Claassen M*,

    Cell Reports. 2020.

  • Dynamic Distribution Decomposition for Single-Cell Snapshot Time Series Identifies Subpopulations and Trajectories during iPSC Reprogramming.

    Taylor-King JP, Riseth A. Macnair W., Claassen M*.

    PLOS Comput Biol. 2020;16(1):e1007491.

  • GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis.

    Galli E, HartmannFJ, Schreiner B, Ingelfinger F, Arvaniti E, Diebold M, Mrdjen D, van der Meer F, Krieg C, Nimer FA, Sanderson N, Stadelman C, Khademi M, Piehl F, Claassen M, Derfuss T, Olsson T, Becher B.

    Nat Med. 2019;25(8):1290-1300.

  • Tree-ensemble analysis assesses presence of multifurcations in single cell data.

    Macnair W, De Vargas Roditi L, Ganscha S, Claassen M*.

    Mol Syst Biol. 2019;15(3):e8552.

  • Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

    Arvaniti E, Fricker K*, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rüschoff J, Claassen M*.

    Sci Rep. 2018;8(1):12054.

  • Sensitive detection of rare disease-associated cell subsets via representation learning.

    Arvaniti E, Claassen M*.

    Nat Commun. 2017;8:14825.

  • Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells.

    Feigelman JS, Ganscha S, Hastreiter S, Schwarzfischer M, Filipczyk A, Schroeder T, Theis FJ, Marr C, Claassen M*.

    Cell Syst. 2016;3(5):480-490.

Zertifikate und Verbände

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