Proteome coverage prediction with infinite Markov models. - Manfred Claassen; Ruedi Aebersold; Joachim M. Buhmann (Bioinform., 2009)

Design and Validation of Proteome Measurements. - Manfred Claassen (Ausgezeichnete Informatikdissertationen, 2010)

Proteome Coverage Prediction for Integrated Proteomics Datasets. - Manfred Claassen; Ruedi Aebersold; Joachim M. Buhmann (J. Comput. Biol., 2011)

Markov Network Structure Learning via Ensemble-of-Forests Models. - Eirini Arvaniti; Manfred Claassen (UAI, 2014)

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series. - Anna Klimovskaia; Stefan Ganscha; Manfred Claassen (PLoS Comput. Biol., 2016)

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data. - Andreas Kühne; Urs Mayr; Daniel C. Sévin; Manfred Claassen; Nicola Zamboni (PLoS Comput. Biol., 2017)

Coupling weak and strong supervision for classification of prostate cancer histopathology images. - Eirini Arvaniti; Manfred Claassen (CoRR, 2018)

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. - Andreas Kopf; Vincent Fortuin; Vignesh Ram Somnath; Manfred Claassen (CoRR, 2019)

Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming. - Jake P. Taylor-King; Asbjørn Nilsen Riseth; Will Macnair; Manfred Claassen (PLoS Comput. Biol., 2020)

Latent representation learning in biology and translational medicine. - Andreas Kopf; Manfred Claassen (Patterns, 2021)

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data. - Andreas Kopf; Vincent Fortuin; Vignesh Ram Somnath; Manfred Claassen (PLoS Comput. Biol., 2021)

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. - Quentin Garrido; Sebastian Damrich; Alexander Jäger; Dario Cerletti; Manfred Claassen; Laurent Najman; Fred A. Hamprecht (Bioinform., 2022)

psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data. - Will Macnair; Revant Gupta; Manfred Claassen (Bioinform., 2022)

Privacy-preserving federated neural network learning for disease-associated cell classification. - Sinem Sav; Jean-Philippe Bossuat; Juan Ramón Troncoso-Pastoriza; Manfred Claassen; Jean-Pierre Hubaux (Patterns, 2022)

A review on deep learning applications in highly multiplexed tissue imaging data analysis. - Mohammed Zidane; Ahmad Makky; Matthias Bruhns; Alexander Rochwarger; Sepideh Babaei; Manfred Claassen; Christian M. Schürch (Frontiers Bioinform., 2023)

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis. - Mohammed Zidane; Ahmad Makky; Matthias Bruhns; Alexander Rochwarger; Sepideh Babaei; Manfred Claassen; Christian M. Schürch (Frontiers Bioinform., 2023)

S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue. - Sepideh Babaei; Jonathan Christ; Vivek Sehra; Ahmad Makky; Mohammed Zidane; Kilian Wistuba-Hamprecht; Christian M. Schürch; Manfred Claassen (Patterns, 2023)

DGP-LVM: Derivative Gaussian process latent variable models. - Soham Mukherjee; Manfred Claassen; Paul-Christian Bürkner (Stat. Comput., 2025)