Author

Charlotte Würtzen

University of OsloORCID 0000-0002-7472-6985Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:CITE-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Chakravarthi KanduriiD· 2 shared Geir Kjetil SandveiD· 2 shared Maria MamicaiD· 2 shared Milena PavlovićiD· 2 shared Victor GreiffiD· 2 shared Bjoern PetersiD· 1 shared Christin Lund‐AnderseniD· 1 shared John Mark B. GubataniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersImmunology

Chakravarthi KanduriORCID

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2 papers

Recent·Most cited

What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study

Charlotte Würtzen, Maria Mamica, Chakravarthi Kanduri, Milena Pavlović, Victor Greiff, Bjoern Peters, Geir Kjetil Sandve

This study benchmarks generative models for adaptive immune receptor repertoires, identifying their strengths and limitations in capturing biological features.

bioRxiv · Jul 16, 2026 · PreprintImmunology

Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

Milena Pavlović, Charlotte Würtzen, Chakravarthi Kanduri, Maria Mamica, Lonneke Scheffer, Christin Lund‐Andersen, John Mark B. Gubatan, Theresa Ullmann, Victor Greiff, Geir Kjetil Sandve

This paper introduces immuneML, a framework for unsupervised machine learning in adaptive immune receptor repertoires, demonstrating its use in generative modeling, clustering, and dataset analysis.

bioRxiv · Apr 18, 2026 · PreprintBioinformatics