Author

Чечехина Елизавета Сергеевна

Lomonosov Moscow State UniversityORCID 0000-0002-5377-2712Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Konstantin Yu. KulebyakiniD· 2 shared Denis Y. LogunoviD· 1 shared Dmitry I. TychininiD· 1 shared Dmitry Vladimirovich SvetlichnyyiD· 1 shared Egor Putlyaev· 1 shared L. N. ShcherbakovaiD· 1 shared Maksim A. Vigovskiy· 1 shared Maxim N. KaragyauriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Konstantin Yu. KulebyakinORCID

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2 papers

Recent·Most cited

BioinformaticsNucleic Acids ResearchPeer-reviewedJun 4, 2026

scParadise: Tunable highly accurate multi-task cell type annotation and surface protein abundance prediction

Чечехина Елизавета Сергеевна, L. N. Shcherbakova, Maksim A. Vigovskiy…·Vadim I. Chechekhin

scParadise provides tools for accurate cell type annotation and surface protein prediction in scRNA-seq data, improving performance for rare cell types and clustering.

DOI 10.1093/nar/gkag612

scRNA-seqdeep learning

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BioinformaticsbioRxivPreprintMay 13, 2025

NuclePhaser: a YOLO-based framework for cell nuclei detection and counting in phase contrast images of arbitrary size with support of fast calibration and testing on specific use cases

Волошин Никита Сергеевич, Egor Putlyaev, Чечехина Елизавета Сергеевна…·Konstantin Yu. Kulebyakin

NuclePhaser uses YOLO models to detect and count cell nuclei in phase contrast images, enabling fast, calibration-free analysis across diverse cell types and imaging conditions.

3 citations

imagingdeep learning

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