| Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ... Jama 318 (22), 2199-2210, 2017 | 3969 | 2017 |
| 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 | 838 | 2017 |
| Classifying and segmenting microscopy images with deep multiple instance learning OZ Kraus, JL Ba, BJ Frey Bioinformatics 32 (12), i52-i59, 2016 | 557 | 2016 |
| Automated analysis of high‐content microscopy data with deep learning OZ Kraus, BT Grys, J Ba, Y Chong, BJ Frey, C Boone, BJ Andrews Molecular systems biology 13 (4), 924, 2017 | 339 | 2017 |
| Machine learning and computer vision approaches for phenotypic profiling BT Grys, DS Lo, N Sahin, OZ Kraus, Q Morris, C Boone, BJ Andrews Journal of Cell Biology 216 (1), 65-71, 2017 | 201 | 2017 |
| Exploring whole-genome duplicate gene retention with complex genetic interaction analysis E Kuzmin, B VanderSluis, AN Nguyen Ba, W Wang, EN Koch, M Usaj, ... Science 368 (6498), eaaz5667, 2020 | 128 | 2020 |
| 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 | 116 | 2019 |
| Masked autoencoders for microscopy are scalable learners of cellular biology O Kraus, K Kenyon-Dean, S Saberian, M Fallah, P McLean, J Leung, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 78 | 2024 |
| Computer vision for high content screening OZ Kraus, BJ Frey Critical reviews in biochemistry and molecular biology 51 (2), 102-109, 2016 | 62 | 2016 |
| Rxrx1: A dataset for evaluating experimental batch correction methods M Sypetkowski, M Rezanejad, S Saberian, O Kraus, J Urbanik, J Taylor, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 58 | 2023 |
| Rxrx3: Phenomics map of biology MM Fay, O Kraus, M Victors, L Arumugam, K Vuggumudi, J Urbanik, ... Biorxiv, 2023.02. 07.527350, 2023 | 39 | 2023 |
| Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins AX Lu, YT Chong, IS Hsu, B Strome, LF Handfield, O Kraus, BJ Andrews, ... Elife 7, e31872, 2018 | 35 | 2018 |
| Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle A Litsios, BT Grys, OZ Kraus, H Friesen, C Ross, MPD Masinas, ... Cell 187 (6), 1490-1507. e21, 2024 | 32 | 2024 |
| System and method for classifying and segmenting microscopy images with deep multiple instance learning O KRAUS, BA Jimmy, B Frey US Patent 10,303,979, 2019 | 28 | 2019 |
| Masked autoencoders are scalable learners of cellular morphology O Kraus, K Kenyon-Dean, S Saberian, M Fallah, P McLean, J Leung, ... arXiv preprint arXiv:2309.16064, 2023 | 21 | 2023 |
| Vitally consistent: Scaling biological representation learning for cell microscopy K Kenyon-Dean, ZJ Wang, J Urbanik, K Donhauser, J Hartford, ... Proceedings of the 42nd International Conference on Machine Learning, PMLR …, 2024 | 9 | 2024 |
| RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy O Kraus, F Comitani, J Urbanik, K Kenyon-Dean, L Arumugam, ... LMRL Workshop at ICLR 2025, 2025 | 4 | 2025 |
| Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention R Dandage, M Papkov, BM Greco, D Fishman, H Friesen, K Wang, ... bioRxiv, 2023 | 3 | 2023 |
| Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings OZ Kraus, KR Kenyon-Dean, M Saberian, M Fallah, PF McLean, ... US Patent 12,119,090, 2024 | 2 | 2024 |
| System and method for profiling antibodies with high-content screening (hcs) S Cooper, O KRAUS, M LONDON, G WATSON, A NIXON, E Koch, ... US Patent App. 17/918,882, 2023 | 2 | 2023 |
Brenda AndrewsDirector, The Donnelly Centre, University of TorontoVerified email at utoronto.ca
