Author

Alan O’Callaghan

Institute of Genetics and CancerORCID 0000-0003-4817-6171Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Peter BankheadiD· 2 shared Andrew J. FilbyiD· 1 shared Bradley T. HarrisiD· 1 shared Farhat V. N. DiniD· 1 shared Filip Bušić· 1 shared Fiona InglisiD· 1 shared Hakan BileniD· 1 shared James DoddiD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersMolecular Biology

Peter BankheadORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Molecular BiologyNature CommunicationsPeer-reviewedMay 5, 2025

GelGenie: an AI-powered framework for gel electrophoresis image analysis

Matthew Aquilina, Nathan J. W. Wu, Kiros Kwan…·Katherine E. Dunn

GelGenie is an AI framework that automatically identifies gel bands in electrophoresis images, outperforming existing software and matching manual analysis.

15 citationstop 3%

imagingdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintSep 4, 2024

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

Thibaut Goldsborough, Alan O’Callaghan, Fiona Inglis…·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.

27 citations

imagingdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMar 29, 2022

Transcriptional dynamics of colorectal cancer risk associated variation at 11q23.1 are correlated with tuft cell abundance and marker expression in silico

Bradley T. Harris, Vidya Rajasekaran, James Peter Blackmur…·Susan Mary Farrington

The paper investigates the relationship between 11q23.1 eQTL targets and tuft cell abundance using scRNA-seq data, finding a correlation between gene expression and cluster-specific tuft cell markers.

DOI 10.1101/2022.03.29.485182

humanscRNA-seq

𝕏 PostBluesky