Author

Sigrid Leyendecker

Friedrich-Alexander-Universität Erlangen-NürnbergORCID 0000-0002-8585-2725Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Assay:metabolomics· 1

Frequent Co-Authors

Denisa MartonováiD· 2 shared Ellen Kuhl AiD· 2 shared Gerhard A. HolzapfeliD· 2 shared Kevin LinkaiD· 1 shared Mathias PeirlinckiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersBioengineering

Denisa MartonováORCID

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2 papers

Recent·Most cited

BioengineeringBiomechanics and Modeling in MechanobiologyPeer-reviewedAug 27, 2025

Discovering dispersion: How robust is automated model discovery for human myocardial tissue?

Denisa Martonová, Sigrid Leyendecker, Gerhard A. Holzapfel, Ellen Kuhl A

This study evaluates the robustness of automated model discovery in human myocardial tissue by incorporating generalized structure tensors to account for fiber dispersion.

9 citations

humanmetabolomics

𝕏 PostBluesky

BioengineeringComputer Methods in Applied Mechanics and EngineeringPeer-reviewedMay 29, 2024

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

Denisa Martonová, Mathias Peirlinck, Kevin Linka…·Ellen Kuhl A

This paper automates cardiac tissue model discovery by training a neural network to identify the best model and parameters for human cardiac tissue.

50 citationstop 3%

humandeep learning

𝕏 PostBluesky