Author

Hakan Bilen

University of EdinburghORCID 0000-0002-6947-6918Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Assay:fluorescence / live-cell microscopy· 1 Assay:imaging· 1

Frequent Co-Authors

Alan O’CallaghaniD· 1 shared Aleksandra Sobieska· 1 shared Andrew J. FilbyiD· 1 shared Ben PhilpsiD· 1 shared Constance AlabertiD· 1 shared F. N. C. Smith· 1 shared Fiona InglisiD· 1 shared Koy Chong Ng Kee KwongiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Alan O’CallaghanORCID

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2 papers

Recent·Most cited

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics

Koy Chong Ng Kee Kwong, Ben Philps, F. N. C. Smith, Aleksandra Sobieska, Naiming Chen, Constance Alabert, Hakan Bilen, Sara B.C. Buonomo

This study uses supervised and unsupervised deep learning to analyze DNA replication dynamics in mouse cells, identifying altered patterns in Rif1-deficient cells and extending to patient samples.

Communications Biology · Feb 26, 2025Peer-reviewedCell Biology

A novel channel invariant architecture for the segmentation of cells and nuclei in multiplexed images using InstanSeg

Thibaut Goldsborough, Alan O’Callaghan, Fiona Inglis, Léo Leplat, Andrew J. Filby, Hakan Bilen, Peter Bankhead

The paper introduces ChannelNet, a deep learning architecture for segmenting cells and nuclei in multiplexed images, and demonstrates its effectiveness when combined with InstanSeg.

bioRxiv · Sep 4, 2024 · PreprintBioinformatics