Author

P. Dunne

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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Cancer Biology· 3

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Organism:human· 2 Assay:imaging· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:mouse· 1

Frequent Co-Authors

M. Lawler· 2 shared A. A. Meade· 1 shared A. Berenguer Llergo· 1 shared A. Blake· 1 shared A. Campbell· 1 shared A. Chatzipli· 1 shared A. D. Beggs· 1 shared A. D. Corwin· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersCancer Biology

M. Lawler

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Author profile →

3 papers

Recent·Most cited

MmCMS: Mouse models' Consensus Molecular Subtypes of colorectal cancer

R. Amirkhah, K. Gilroy, S. Malla, T. Lannagan, R. Byrne, N. C. Fisher, S. Corry, H. Naderi-Meshkin, B. Ahmaderaghi, R. Murray, M. Mills, A. Campbell, ACRCelerate consortium, A. Berenguer Llergo, R. Sanz-Pamplona, , P. Dunne

The paper develops three methods to classify mouse colorectal cancer models into human CMS subtypes, finding that pathway-level classifiers outperform gene-level ones for accurate subtype identification.

bioRxiv · Jun 17, 2022Cancer Biology

An atlas of inter- and intra-tumor heterogeneityof apoptosis competency in colorectal cancertissue at single cell resolution

A. U. Lindner, M. Salvucci, E. MCDonough, S. Cho, X. Stachtea, E. P. O'Connell, A. D. Corwin, A. Santamaria-Pang, S. Carberry, M. Fichtner, S. Van Schaeybroeck, P. Laurent-Puig, J. P. Burke, D. McNamara, M. Lawler, , J. H. Prehn

The paper generates an atlas of apoptosis susceptibility heterogeneity in colorectal cancer using single-cell analysis and ODE modeling, identifying intrinsic cellular differences in apoptosis competency.

bioRxiv · Mar 19, 2021Cancer Biology

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

K. Sirinukunwattana, E. Domingo, S. Richman, K. L. Redmond, A. Blake, C. Verrill, S. J. Leedham, A. Chatzipli, C. Hardy, C. Whalley, C.-H. Wu, A. D. Beggs, U. McDermott, P. Dunne, A. A. Meade, , V. Koelzer

This paper develops imCMS, a deep learning method to classify colorectal cancer subtypes from histological images, showing improved accuracy over bioinformatic methods.

bioRxiv · May 23, 2019Cancer Biology