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

Click to filter author papers

Cancer Biology· 4

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 4 Approach:deep learning· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Approach:experimental assay· 1

Frequent Co-Authors

A. A. Meade· 1 shared A. B. Gill· 1 shared A. Blake· 1 shared A. Butler· 1 shared A. Chatzipli· 1 shared A. D. Beggs· 1 shared A. Durrani· 1 shared A. Gogleva· 1 shared

Activity by Year

2019-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 10 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

1 shared paperCancer Biology

A. A. Meade

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

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