Author

Alexander Kensert

Ghent UniversityORCID 0000-0002-5295-010XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1

Frequent Co-Authors

Alireza NameniiD· 1 shared Arthur DeclercqiD· 1 shared Jiřı́ UrbaniD· 1 shared Kateřina HrůzováiD· 1 shared Lennart MartensiD· 1 shared Ola SpjuthiD· 1 shared Philip John HarrisoniD· 1 shared Ralf GabrielsiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

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10 top collaborators · 29 mutual co-author links

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1 shared paperBioinformatics

Alireza NameniORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 20, 2026

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

Alexander Kensert, Kateřina Hrůzová, Robbe Devreese…·Jiřı́ Urban

The paper introduces a deep learning model to predict peptide retention times in liquid chromatography and identifies key factors influencing retention through saliency mapping.

DOI 10.64898/2026.05.18.725893

proteomicsdeep learning

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BioinformaticsSLAS DISCOVERYPeer-reviewedJan 14, 2019

Transfer learning with deep convolutional neural networks for classifying cellular morphological changes

Alexander Kensert, Philip John Harrison, Ola Spjuth

This study uses transfer learning with pre-trained deep convolutional neural networks to classify cellular morphological changes from drug-treated cells, achieving 95-97% accuracy.

165 citationstop 1%

imagingdeep learning

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