Author

Yalin Zheng

University of LiverpoolORCID 0000-0002-7873-0922Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yalin Zheng1 paper · European Bioinformatics Institute

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· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Alex Alves FreitasiD· 1 shared D. WilkinsoniD· 1 shared David A. TurneriD· 1 shared Gustavo Daniel Vega Magdaleno· 1 shared João Pedro de MagalhãesiD· 1 shared Kai F. HoettgesiD· 1 shared Meagan J. Hennessy· 1 shared Michael WhiteiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Alex Alves FreitasORCID

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2 papers

Recent·Most cited

BioinformaticsBMC BiologyPeer-reviewedMay 11, 2025

Domain Specific AI Segmentation of IMPDH2 Rod/Ring Structures in Mouse Embryonic Stem Cells

Sam T. M. Ball, Meagan J. Hennessy, Yuhan Tan…·David A. Turner

The paper develops an AI pipeline to automatically segment IMPDH2 rod-ring structures in mouse ESCs, achieving high accuracy and agreement with expert annotations.

1 citation

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BioinformaticsbioRxivPreprintJul 17, 2021

Machine-learning-based predictions of caloricrestriction associations across ageing-related genes

Gustavo Daniel Vega Magdaleno, Vladislav Bespalov, Yalin Zheng…·João Pedro de Magalhães

This paper uses machine learning to classify ageing-related genes as CR-related or not based on various predictive features, finding that curated knowledge features are most predictive.

DOI 10.1101/2021.07.17.452785

machine learning (classical)cell type annotation

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