samuel cooper
samuel cooper
PhD student, Institute of cancer research, Imperial College London
Verified email at
Cited by
Cited by
Data-analysis strategies for image-based cell profiling
JC Caicedo, S Cooper, F Heigwer, S Warchal, P Qiu, C Molnar, ...
Nature methods 14 (9), 849-863, 2017
DNA damage during S-phase mediates the proliferation-quiescence decision in the subsequent G1 via p21 expression
AR Barr, S Cooper, FS Heldt, F Butera, H Stoy, J Mansfeld, B Novák, ...
Nature communications 8 (1), 14728, 2017
Nanoneedle-mediated stimulation of cell mechanotransduction machinery
CS Hansel, SW Crowder, S Cooper, S Gopal, M Joăo Pardelha da Cruz, ...
ACS nano 13 (3), 2913-2926, 2019
Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting
AX Lu, OZ Kraus, S Cooper, AM Moses
PLoS computational biology 15 (9), e1007348, 2019
A comprehensive model for the proliferation–quiescence decision in response to endogenous DNA damage in human cells
FS Heldt, AR Barr, S Cooper, C Bakal, B Novák
Proceedings of the National Academy of Sciences 115 (10), 2532-2537, 2018
Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity
H Sailem, V Bousgouni, S Cooper, C Bakal
Open biology 4 (1), 130132, 2014
RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer
P Pascual-Vargas, S Cooper, J Sero, V Bousgouni, M Arias-Garcia, ...
Scientific data 4 (1), 1-13, 2017
Apolar and polar transitions drive the conversion between amoeboid and mesenchymal shapes in melanoma cells
S Cooper, A Sadok, V Bousgouni, C Bakal
Molecular biology of the cell, mbc. E15-06-0382, 2015
NucliTrack: an integrated nuclei tracking application
S Cooper, AR Barr, R Glen, C Bakal
Bioinformatics 33 (20), 3320-3322, 2017
Accelerating live single-cell signalling studies
S Cooper, C Bakal
Trends in biotechnology 35 (5), 422-433, 2017
Visualizing quantitative microscopy data: History and challenges
HZ Sailem, S Cooper, C Bakal
Critical reviews in biochemistry and molecular biology 51 (2), 96-101, 2016
Society of biomolecular imaging and informatics high-content screening/high-content analysis emerging technologies in biological models, when and why?
AF Hoffman, J Nolan, DF Gebhard, D Nickischer, W Omta, S Cooper, ...
ASSAY and Drug Development Technologies 16 (1), 1-6, 2018
scMARK an ‘MNIST’like benchmark to evaluate and optimize models for unifying scRNA data
Swechha, D Mendonca, O Focsa, JJ Díaz-Mejía, S Cooper
bioRxiv, 2021.12. 08.471773, 2021
Learning Biology Through Puzzle-solving: Unbiased Automatic Understanding of Microscopy Images with Self-supervised Learning
A Lu, O Kraus, S Cooper, A Moses
Microscopy and Microanalysis 26 (S2), 690-692, 2020
AI/ML-driven discovery of CTHRC1, collagen triple helix repeat-containing 1, a novel proteoglycan for stroma+ tumor targeting and delivery of 4-1BB costimulation
C Harvey, E Koch, A Hanson, L Rice, A Berkley, K White, R Saberianfar, ...
Cancer Research 84 (6_Supplement), 2914-2914, 2024
System and method for profiling antibodies with high-content screening (hcs)
S Cooper, O Kraus, M London, G Watson, A Nixon, E Koch, Č Dumoulin, ...
US Patent App. 17/918,882, 2023
Unification of over 50 published single-cell RNA datasets covering over a 1000 patient samples with deep-learning reveals novel axes of tumor microenvironment variation
J Díaz-Mejía, D Mendonca, O Focsa, C Harvey, M Briskin, S Cooper
Cancer Research 82 (12_Supplement), 3813-3813, 2022
1350 AI/ML-driven discovery of Collagen Triple Helix Repeat-Containing 1, CTHRC1, a novel proteoglycan for disruption of stromal barriers and enhancement of immunotherapy …
E Koch, A Berkley, S Phippen, M London, K White, A Hanson
J Immunother 10 (2), A1-A1595, 2022
R Schuster, O Kraus, S Cooper, B Hinz
Wound Repair and Regeneration 28 (2), A4-A4, 2020
A single cell approach to understanding cell cycle entry in cancer
A Barr, FS Heldt, S Cooper, B Novak, C Bakal
European Journal of Cancer 1 (61), S172, 2016
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