Author

U. McDermott

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· 4 Cell Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 5 Approach:deep learning· 3 Research Focus:differential expression· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Research Focus:variant calling· 2

Frequent Co-Authors

A. A. Meade· 1 shared A. B. Gill· 1 shared A. Blake· 1 shared A. Butler· 1 shared A. Cagan· 1 shared A. Chatzipli· 1 shared A. Clay· 1 shared A. D. Beggs· 1 shared

Activity by Year

2019-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 8 mutual co-author links

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1 shared paperCancer Biology

A. A. Meade

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Author profile →

5 papers

Recent·Most cited

The long-term effects of chemotherapy on normal blood cells

E. Mitchell, M. H. Pham, A. Clay, R. Sanghvi, S. Pietsch, J. I. Hsu, H. Jung, A. Vedi, S. Moody, J. Wang, D. Leonganmornlert, M. Spencer Chapman, N. Williams, E. Dunstone, A. Santarsieri, , M. R. Stratton

This study examines the long-term effects of chemotherapy on normal blood cells by analyzing mutation burdens and cell population structure in treated individuals.

bioRxiv · May 20, 2024Cell Biology

Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer

A. Gogleva, D. Polychronopoulos, M. Pfeifer, V. Poroshin, M. Ughetto, B. Sidders, J. Dry, M. Ahdesmaki, U. McDermott, E. Papa, K. Bulusu

The paper develops a knowledge graph-based recommendation system to identify novel resistance drivers in EGFR mutant NSCLC, reducing hit identification time from months to minutes.

bioRxiv · Jul 23, 2021Cancer Biology

High-sensitivity monitoring of ctDNA by patient-specific sequencing panels and integration of variant reads

J. C. Wan, K. Heider, D. Gale, S. Murphy, E. Fisher, J. Morris, F. Mouliere, D. Chandrananda, A. Marshall, A. B. Gill, P. Y. Chan, E. Barker, G. Young, W. N. Cooper, I. Hudecova, , N. Rosenfeld

This paper introduces INVAR, a method to improve ctDNA detection sensitivity using patient-specific sequencing panels and variant read integration, achieving ppm-level detection in clinical samples.

bioRxiv · Sep 11, 2019 · PreprintCancer 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

Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening

M. Garnett, G. Picco, E. Chen, L. Garcia Alonso, F. Behan, E. Goncalves, G. Bignell, A. Matchan, B. Fu, R. Banerjee, E. Anderson, A. Butler, C. Benes, U. McDermott, D. Dow, , J. Saez-Rodriguez

The paper functionally links gene fusions to cancer cell fitness using drug sensitivity and CRISPR-Cas9 data, identifying rare functional fusions and novel therapeutic targets.

bioRxiv · Feb 24, 2019Cancer Biology