Author

Maria Mamica

University of OsloORCID 0009-0005-7846-4897Unclaimed 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 Charlotte WürtzeniD· 2 shared Geir Kjetil SandveiD· 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

ImmunologybioRxivPreprintJul 16, 2026

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

Charlotte Würtzen, Maria Mamica, Chakravarthi Kanduri…·Geir Kjetil Sandve

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

DOI 10.64898/2026.07.10.737788

proteomicsdeep learning

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BioinformaticsbioRxivPreprintApr 18, 2026

Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

Milena Pavlović, Charlotte Würtzen, Chakravarthi Kanduri…·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.

1 citation

CITE-seqmachine learning (classical)

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