Author

P. Quirke

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· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:human· 2 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:imaging· 1

Frequent Co-Authors

A. A. Meade· 1 shared A. Blake· 1 shared A. Chatzipli· 1 shared A. D. Beggs· 1 shared C. Hardy· 1 shared C. Verrill· 1 shared C. Whalley· 1 shared C. Young· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

A. A. Meade

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2 papers

Recent·Most cited

Analysis of an Indian colorectal cancer faecal microbiome collection demonstrates universal colorectal cancer-associated patterns, but closest correlation with other Indian cohorts.

M. Bose, H. M. Wood, C. Young, International CRC Microbiome Network (AMS/CRUK), P. Quirke, R. A. Seshadri

The paper analyzes an Indian colorectal cancer fecal microbiome cohort, showing global CRC-associated patterns but highlighting regional variations and the importance of local studies.

BMC Microbiology · Mar 2, 2023Peer-reviewedCancer 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