Author

Anastasia Litinetskaya

Helmholtz MunichORCID 0000-0003-2977-6374Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:scRNA-seq· 2 Assay:spatial transcriptomics· 2 Research Focus:batch correction / integration· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1 Approach:deep learning· 1

Frequent Co-Authors

Fabian Joachim TheisiD· 2 shared Mohammad LotfollahiiD· 2 shared Alireza OmidiiD· 1 shared Amir Ali MoinfariD· 1 shared Artur SzałataiD· 1 shared Fabiola CurioniD· 1 shared Maiia ShulmaniD· 1 shared Soroor Hediyeh-zadehiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Fabian Joachim TheisORCID

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2 papers

Recent·Most cited

BioinformaticsLecture notes in computer sciencePeer-reviewedJan 1, 2025

Integration and querying of multimodal single-cell data with PoE-VAE

Anastasia Litinetskaya, Maiia Shulman, Fabiola Curion…·Fabian Joachim Theis

This paper introduces a VAE-based model for integrating multimodal single-cell data, showing it can map queries across modalities and improve data imputation and biological discovery.

64 citationstop 1%

humanscRNA-seq

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BioinformaticsbioRxivPreprintJul 29, 2024

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Anastasia Litinetskaya, Soroor Hediyeh-zadeh, Amir Ali Moinfar…·Fabian Joachim Theis

The paper introduces MultiMIL, a deep learning framework for phenotype prediction and cell state identification using single-cell data from COVID-19 patients and other datasets.

20 citations

humanscRNA-seq

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