Author

Tim Lenz

Institut Gustave RoussyORCID 0000-0002-9034-2535Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:diffusion model· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Jakob Nikolas KatheriD· 2 shared Laura ŽigutytėiD· 2 shared Michaela UngeriD· 2 shared Zunamys Itzell CarreroiD· 2 shared Alexander T. PearsoniD· 1 shared Andreas MöckiD· 1 shared Asier Rabasco MeneghettiiD· 1 shared Carolin Victoria SchneideriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioinformatics

Jakob Nikolas KatherORCID

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2 papers

Recent·Most cited

BioinformaticsCancer ResearchPeer-reviewedJun 17, 2026

Counterfactual Diffusion Models for Interpretable Explanations of Artificial Intelligence Models in Pathology

Laura Žigutytė, Tim Lenz, Tianyu Han…·Jakob Nikolas Kather

MoPaDi generates counterfactual explanations for histopathology images to reveal morphological features driving classifier predictions, improving interpretability in digital pathology.

DOI 10.1158/0008-5472.can-25-5645

humanimaging

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BioinformaticsbioRxivPreprintJan 6, 2025

End-to-end deep learning versus machine learning for biomarker discovery in cancer genomes

Michaela Unger, Chiara Maria Lavinia Loeffler, Julien Vibert…·Jakob Nikolas Kather

This study compares deep learning and machine learning for biomarker prediction in cancer genomes, finding DL performs better in cross-assay and data-sparse settings.

3 citations

humanwhole-exome sequencing

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