Author

Friedrich-Maximilian Weberling

University of CambridgeORCID 0009-0005-9480-7888Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Research Focus:protein design· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Florian HollfelderiD· 2 shared Daniel Boros· 1 shared Elie Patel· 1 shared Elliot J. Medcalf· 1 shared Francesco G. MuttiiD· 1 shared Friederike E. H. NintzeliD· 1 shared Ioakeim AmpartzidisiD· 1 shared Irina I MohorianuiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersCell Biology

Florian HollfelderORCID

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2 papers

Recent·Most cited

Cell BiologybioRxivPreprintOct 3, 2026

Deep generative embeddings of gene expression and splicing reposition the interpretation of single-cell transcriptomic signatures

Friedrich-Maximilian Weberling, Ioakeim Ampartzidis, Irina I Mohorianu, Florian Hollfelder

Crecerelle integrates gene expression and alternative splicing to improve cell-type annotation and uncover regulatory pathways in single-cell transcriptomics.

DOI 10.64898/2026.09.23.753495

mousescRNA-seq

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Synthetic BiologybioRxivPreprintApr 8, 2024

Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering

Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel…·Florian Hollfelder

The paper combines microfluidic screening with machine learning to rapidly engineer biocatalysts, achieving 23-fold improvement in catalytic efficiency through sequence-function mapping.

18 citations

machine learning (classical)protein design

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