Author

N. Carragher

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cell Biology· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:drug response / drug discovery· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Irving· 1 shared A. Kapourani· 1 shared A. Quintanilla· 1 shared C. A. Vallejos· 1 shared C. J. Weston· 1 shared D. A. Oyarzun· 1 shared E. F. Sutherland· 1 shared E. Zajdel· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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1 shared paperCell Biology

A. Irving

Author profile →

3 papers

Recent·Most cited

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

L. Martin, A. Irving, Y. Suwanlikit, G. Morrison, R. J. R. Elliott, N. Carragher, S. Pollard, T. Chandra

The paper uses machine learning to identify senescent glioblastoma cells and discovers compounds that induce senescence in these cells.

bioRxiv · Apr 3, 2024 · PreprintCell Biology

Multimodal decoding of human liver regeneration

K. P. Matchett, J. W. Wilson-Kanamori, J. R. Portman, A. Kapourani, F. Fercoq, S. May, J. B. Mackey, M. Brice, E. Zajdel, M. Beltran-Sierra, E. F. Sutherland, G. C. Wilson, S. J. Wallace, L. Kitto, N. T. Younger, , N. C. Henderson

This study uses single-nuclei RNA sequencing and spatial profiling to create a single-cell atlas of human liver regeneration, identifying a novel migratory hepatocyte subpopulation and demonstrating that wound closure precedes hepatocyte proliferation.

bioRxiv · Feb 24, 2023Cell Biology

Discovery of new senolytics using machine learning

V. Smer-Barreto, A. Quintanilla, R. J. Elliot, J. C. Dawson, J. Sun, N. Carragher, J. C. Acosta, D. A. Oyarzun

This paper uses machine learning to discover three new senolytic compounds by screening FDA-approved drugs and clinical candidates.

bioRxiv · Apr 26, 2022 · PreprintCancer Biology