Author

Viktoria Schuster

University of CopenhagenORCID 0000-0002-6016-8337Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Approach:deep learning· 3 Organism:human· 3 Research Focus:differential expression· 2 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Anders KroghiD· 3 shared Asta Mannstaedt RasmusseniD· 1 shared Emma DanniD· 1 shared Farhad ZamaniiD· 1 shared Iñigo Prada-LuengoiD· 1 shared Jakob Skou PederseniD· 1 shared Mathilde Hartvig DiekemaiD· 1 shared Sarah Amalia TeichmanniD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersBioinformatics

Anders KroghORCID

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3 papers

Recent·Most cited

A deep generative decoder predicts microRNA expression from bulk and single-cell mRNA profiles

Farhad Zamani, Asta Mannstaedt Rasmussen, Viktoria Schuster, Mathilde Hartvig Diekema, Anders Krogh, Jakob Skou Pedersen

miDGD predicts microRNA expression from bulk and single-cell mRNA profiles using a deep generative decoder, achieving high accuracy across diverse datasets and cell types.

bioRxiv · Sep 10, 2026 · PreprintBioinformatics

multiDGD: A versatile deep generative model for multi-omics data

Viktoria Schuster, Emma Dann, Anders Krogh, Sarah Amalia Teichmann

multiDGD is a deep generative model for multi-omics data that learns shared representations and enables data integration and association detection.

Nature Communications · Nov 20, 2024Peer-reviewedBioinformatics

N-of-one differential gene expression without control samples using a deep generative model

Iñigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, Thilde Terkelsen, Valentina Sora, Anders Krogh

This paper introduces a deep generative model to perform differential gene expression analysis on single cancer samples without control samples, outperforming DESeq2 in detecting differentially expressed genes.

Genome biology · Nov 16, 2023Peer-reviewedBioinformatics