Skip to main content
Research
  1. Queen Mary University of London
  2. Research
  3. Publications

Publications: Mr William Dee

Back to Search
Dee W, Wenteler A, Seal S, Morris O, Slabaugh G ( 2026 ) . SHOT-CCR: Biologically Guided Adversarial Training for Test-Time Adaptation in Cellular Morphology . Medical Image Computing and Computer Assisted Intervention – MICCAI 2026 , vol. 16891 , Springer Nature
Seal S, Dee W, Shah A, Cerisier N, Zhang A, Miglietta E, Titterton K, Cabrera ÁA et al. ( 2026 ) . Counting cells can accurately predict small-molecule bioactivity benchmarks . Nature Communications vol. 17 , ( 1 )
Dee W, Ibrahim RA, Marouli E ( 2024 ) . Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap between thyroid cancer histopathology datasets . PLOS ONE vol. 19 , ( 12 )
Wenteler A, Occhetta M, Branson N, Curean V, Huebner M, Dee W, Connell W, Chung SP et al. . PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction .
Dee W, Sequeira I, Lobley A, Slabaugh G ( 2024 ) . Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data . iScience vol. 27 , ( 8 )
Dee W ( 2022 ) . LMPred: predicting antimicrobial peptides using pre-trained language models and deep learning . Bioinformatics Advances vol. 2 , ( 1 )
Dee W, Sequeira I, Lobley A, Slabaugh G . Cell-Vision Fusion: A Swin Transformer-based Approach to Predicting Kinase Inhibitor Mechanism of Action from Cell Painting Data .
Dee W, Ibrahim RA, Marouli E . Histopathological Domain Adaptation with Generative Adversarial Networks Bridging the Domain Gap Between Thyroid Cancer Histopathology Datasets .
Dee W . LMPred: Predicting Antimicrobial Peptides Using Pre-Trained Language Models and Deep Learning .
Dee W . Machine Learning Methods for Cell Painting Phenotyping in Drug Discovery .
Dee W, Wenteler A, Seal S, Morris O, Slabaugh G . SHOT-CCR: Biologically guided adversarial training for test-time adaptation in cellular morphology .
Seal S, Dee W, Shah A, Zhang A, Titterton K, Cabrera ÁA, Boiko D, Beatson A et al. . Small molecule bioactivity benchmarks are often well-predicted by counting cells .
Back to Search