The EMUNE project has resulted in the following publications:

A latent cardiomyocyte regeneration potential in human heart disease.
Derks, W., et al. (2025).
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Developmental beta-cell death orchestrates the islet’s inflammatory milieu by regulating immune system crosstalk.
Akhtar, M. N., et al. (2025).
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An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation.
Arruda, J., Schälte, Y., Peiter, C., Teplytska, O., Jaehde, U., & Hasenauer, J. (2024).
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Universal differential equations for systems biology: Current state and open problems.
Philipps, M., Schmid, N., & Hasenauer, J. (2024).
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Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.
Dorešić, D., Grein, S., & Hasenauer, J. (2024).
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Tailoring COVID-19 Vaccination Strategies in High-Seroprevalence Settings: Insights from Ethiopia.
Gudina, E. K., et al. (2024).
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A wall-time minimizing parallelization strategy for approximate Bayesian computation.
Alamoudi, E., et al. (2024).
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Missing data in amortized simulation-based neural posterior estimation.
Wang, Z., Hasenauer, J., & Schälte, Y. (2024).
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Collective cell migration due to guidance-by-followers is robust to multiple stimuli.
Müller, R., Boutillon, A., Jahn, D., Starruß, J., David, N. B., & Brusch, L. (2023).
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FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes.
Alamoudi, E., et al. (2023).
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Accessibility of covariance information creates vulnerability in Federated Learning frameworks.
Huth, M., et al. (2023).
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Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation.
Schälte, Y., & Hasenauer, J. (2023).
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pyABC: Efficient and robust easy-to-use approximate Bayesian computation.
Schälte, Y., Klinger, E., Alamoudi, E., & Hasenauer, J. (2022).
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