Author

Karolis Šablauskas

Vilnius UniversityORCID 0000-0003-4166-0331Unclaimed 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 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Bart P. G. H. van der SandeniD· 1 shared Christian GilisseniD· 1 shared Denis BaronasiD· 1 shared Dmitrijs RotsiD· 1 shared Gelana KhazeevaiD· 1 shared Greta LeonavičienėiD· 1 shared Helger G. YntemaiD· 1 shared Justina ŽvirblytėiD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Bart P. G. H. van der SandenORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

High-throughput single cell -omics using semi-permeable capsules

Denis Baronas, Justina Žvirblytė, Simonas Norvaisis, Greta Leonavičienė, Karolis Goda, Vincenta Mikulenaite, Vytautas Kašėta, Karolis Šablauskas, Laimonas Griškevičius, Simonas Juzėnas, Linas Mažutis

This paper introduces a semi-permeable capsule technology for high-throughput single-cell -omics, demonstrating its utility in scRNA-Seq of hematopoietic disorders and uncovering transcriptome changes in AML.

Science · Dec 18, 2025Peer-reviewedGenomics

DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

Gelana Khazeeva, Karolis Šablauskas, Bart P. G. H. van der Sanden, Wouter Steyaert, Michael P. Kwint, Dmitrijs Rots, Max Hinne, Marcel A. J. van Gerven, Helger G. Yntema, Lisenka E.L.M. Vissers, Christian Gilissen

DeNovoCNN is a deep learning tool for de novo variant calling in sequencing data, achieving high recall and precision compared to existing methods.

Nucleic Acids Research · Jun 17, 2022Peer-reviewedBioinformatics