| Intertumoral heterogeneity within medulloblastoma subgroups FMG Cavalli, M Remke, L Rampasek, J Peacock, DJH Shih, B Luu, ... Cancer cell 31 (6), 737-754. e6, 2017 | 1164 | 2017 |
| Recipe for a general, powerful, scalable graph transformer L Rampášek, M Galkin, VP Dwivedi, AT Luu, G Wolf, D Beaini Advances in Neural Information Processing Systems 35, 14501-14515, 2022 | 585 | 2022 |
| Machine learning approaches to drug response prediction: challenges and recent progress G Adam, L Rampášek, Z Safikhani, P Smirnov, B Haibe-Kains, ... NPJ precision oncology 4 (1), 19, 2020 | 319 | 2020 |
| TensorFlow: biology’s gateway to deep learning? L Rampasek, A Goldenberg Cell systems 2 (1), 12-14, 2016 | 298 | 2016 |
| Long range graph benchmark VP Dwivedi, L Rampášek, M Galkin, A Parviz, G Wolf, AT Luu, D Beaini Advances in Neural Information Processing Systems 35, 22326-22340, 2022 | 217 | 2022 |
| Dr. VAE: improving drug response prediction via modeling of drug perturbation effects L Rampášek, D Hidru, P Smirnov, B Haibe-Kains, A Goldenberg Bioinformatics 35 (19), 3743-3751, 2019 | 165 | 2019 |
| Attending to graph transformers L Müller, M Galkin, C Morris, L Rampášek arXiv preprint arXiv:2302.04181, 2023 | 102 | 2023 |
| Dropout feature ranking for deep learning models CH Chang, L Rampasek, A Goldenberg arXiv preprint arXiv:1712.08645, 2017 | 66 | 2017 |
| Dr. vae: Drug response variational autoencoder L Rampasek, D Hidru, P Smirnov, B Haibe-Kains, A Goldenberg arXiv preprint arXiv:1706.08203, 2017 | 38 | 2017 |
| Learning from everyday images enables expert-like diagnosis of retinal diseases L Rampasek, A Goldenberg Cell 172 (5), 893-895, 2018 | 31 | 2018 |
| Gps++: An optimised hybrid mpnn/transformer for molecular property prediction D Masters, J Dean, K Klaser, Z Li, S Maddrell-Mander, A Sanders, H Helal, ... arXiv preprint arXiv:2212.02229, 2022 | 29 | 2022 |
| Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing L Rampášek, A Arbabi, M Brudno Bioinformatics 30 (12), i212-i218, 2014 | 21 | 2014 |
| RNA motif search with data-driven element ordering L Rampášek, RM Jimenez, A Lupták, T Vinař, B Brejová BMC bioinformatics 17, 1-10, 2016 | 18 | 2016 |
| Taxonomy of benchmarks in graph representation learning R Liu, S Cantürk, F Wenkel, S McGuire, X Wang, A Little, L O’Bray, ... Learning on Graphs Conference, 6: 1-6: 25, 2022 | 17 | 2022 |
| Hierarchical graph neural nets can capture long-range interactions L Rampášek, G Wolf arXiv preprint arXiv:2107.07432, 2021 | 15 | 2021 |
| Graph positional and structural encoder S Cantürk, R Liu, O Lapointe-Gagné, V Létourneau, G Wolf, D Beaini, ... arXiv preprint arXiv:2307.07107, 2023 | 14 | 2023 |
| Assessing therapy response in patient-derived xenografts J Ortmann, L Rampášek, E Tai, AS Mer, R Shi, EL Stewart, C Mascaux, ... Science Translational Medicine 13 (620), eabf4969, 2021 | 12 | 2021 |
| Discovery of RNA motifs using a computational pipeline that allows insertions in paired regions and filtering of candidate sequences RM Jimenez, L Rampášek, B Brejová, T Vinař, A Lupták Ribozymes: Methods and Protocols, 145-158, 2012 | 11 | 2012 |
| Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction A Arbabi, L Rampášek, M Brudno Bioinformatics 32 (11), 1662-1669, 2016 | 10 | 2016 |
| Gps++: Reviving the art of message passing for molecular property prediction D Masters, J Dean, K Klaser, Z Li, S Maddrell-Mander, A Sanders, H Helal, ... arXiv preprint arXiv:2302.02947, 2023 | 9 | 2023 |
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of Toronto在 utoronto.ca 的电子邮件经过验证