Author

Michael Habeck

Jena University HospitalORCID 0000-0002-2188-5667Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 3 Research Focus:cell type annotation· 1 Assay:electronic health records· 1 Assay:Hi-C· 1 Organism:human· 1 Assay:imaging· 1 Approach:statistical model· 1

Frequent Co-Authors

Antje EbertiD· 1 shared Benjamin EltzneriD· 1 shared Bert de GrootiD· 1 shared Cleophas Cheruiyot· 1 shared Daniel RudolfiD· 1 shared Daria Plota· 1 shared Fabian PauliD· 1 shared Hafiza Nosheen Saleem· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperBioinformatics

Antje EbertORCID

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4 papers

Recent·Most cited

Sarcomere analysis in human cardiomyocytes by computing radial frequency spectra

Michael Habeck, Hafiza Nosheen Saleem, Daria Plota, Cleophas Cheruiyot, Tobias Kohl, Stephan E. Lehnart, Stefan Jakobs, Antje Ebert

This paper introduces a computational method to analyze sarcomere properties in human cardiomyocytes using radial frequency spectra, showing its effectiveness in detecting molecular differences in cardiac diseases.

Biological Chemistry · Dec 27, 2025Peer-reviewedBioinformatics

Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models

Jan L. Münch, Ralf Schmauder, Fabian Paul, Michael Habeck

The paper uses Bayesian priors to address non-identifiability in HMMs applied to patch clamp data, improving inference accuracy with minimal prior assumptions.

bioRxiv · Apr 20, 2024 · PreprintBioinformatics

Bayesian Maximum Entropy Ensemble Refinement

Benjamin Eltzner, Julian Hofstadler, Daniel Rudolf, Michael Habeck, Bert de Groot

This paper introduces a Bayesian method to refine molecular dynamics simulations using nuclear Overhauser effect measurements by estimating maximum entropy parameters through adaptive Markov Chain Monte Carlo methods.

bioRxiv · Sep 12, 2023 · PreprintBioinformatics

Bayesian inference of chromatin structure ensembles from population Hi-C data

Simeon Carstens, Michaël Nilges, Michael Habeck

The paper introduces a Bayesian method to infer chromatin structure ensembles from population Hi-C data, accounting for cell-to-cell variability.

Proceedings of the National Academy of Sciences · Mar 19, 2020Peer-reviewedBioinformatics