Author
University Hospital ZurichORCID 0000-0002-2279-6906Unclaimed ORCID profile · follow to get notified of new preprints
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5 papers
Recent·Most cited
Maximilian Zwiebel, Maria Wahle, Rudolf Stadler, Mitchell Paul Levesque, Reinhard Dummer, Thierry M. Nordmann, Matthias Mann
The paper demonstrates a method to simultaneously measure genome and proteome from the same cells in archival FFPE tissue, applied to melanoma.
bioRxiv · Jul 10, 2026 · PreprintBiochemistry
Ramon Staeger, Aizhan Tastanova, Adhideb Ghosh, Paul Gueguen, Isabel Kolm, Egle Ramelyte, Evelyn Lattmann, Veronika Haunerdinger, Tugay Karakaya, Marta Slaufova, Michela Di Filippo, Hans‐Dietmar Beer, Mitchell Paul Levesque, Reinhard Dummer
The paper identifies a pre-invasive malignant keratinocyte population in actinic keratosis, characterized by p63 and PITX1 regulation, and distinguishes early oncogenic programs from invasion effectors in squamous cell carcinoma.
bioRxiv · May 24, 2026 · PreprintCancer Biology
Mateusz S. Wietecha, David Lauenstein, Michael Cangkrama, Sybille Seiler, Juyoung Jin, Andreas Goppelt, Manfred Claassen, Mitchell Paul Levesque, Reinhard Dummer, Sabine Werner
The paper identifies wound fibroblast phases and matrix patterns that define cancer-associated fibroblast subtypes with prognostic relevance in skin cancer.
Matrix Biology · Mar 11, 2023Peer-reviewedGenomics
Michael Prummer, Anne Bertolini, Lars Bosshard, Florian Barkmann, Josephine Yates, Valentina Boeva, Ruedi H. Aebersold, Melike Ak, Faisal Alquaddoomi, Jonas B. Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, , Berend Snijder
scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.
NAR Genomics and Bioinformatics · Jun 1, 2022Peer-reviewedBioinformatics
Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Ruedi H. Aebersold, Faisal Alquaddoomi, Jonas B. Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck‐Schimmer, , Vipin T. Sreedharan
SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.
Bioinformatics · Sep 14, 2020Peer-reviewedBioinformatics