2023 arXiv GPS++: Reviving the Art of Message Passing for Molecular Property Prediction Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, and 5 more authors arXiv:2302.02947, 2023 arXiv Code arXiv Attending to Graph Transformers Luis Müller, Mikhail Galkin, Christopher Morris, and Ladislav Rampášek arXiv:2302.04181, 2023 arXiv Code 2022 NeurIPS Recipe for a general, powerful, scalable graph transformer Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini In 36th Conference on Neural Information Processing Systems (NeurIPS), 2022 arXiv Blog Code LoG Taxonomy of benchmarks in graph representation learning Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O’Bray, and 5 more authors In Learning on Graphs Conference, 2022 arXiv Code NeurIPS Long Range Graph Benchmark Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini In 36th Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022 arXiv Blog Code arXiv GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, and 3 more authors arXiv:2212.02229, 2022 arXiv Blog Code Website 2021 IEEE Hierarchical graph neural nets can capture long-range interactions Ladislav Rampášek, and Guy Wolf In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021 HTML Code Slides arXiv Towards a Taxonomy of Graph Learning Datasets Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, and 6 more authors arXiv:2110.14809, 2021 arXiv Code Science Assessing therapy response in patient-derived xenografts Janosch Ortmann, Ladislav Rampášek, Elijah Tai, Arvind Singh Mer, Ruoshi Shi, Erin L Stewart, Celine Mascaux, and 4 more authors Science Translational Medicine, 2021 HTML Code Website 2020 Nature Machine learning approaches to drug response prediction: challenges and recent progress George Adam, Ladislav Rampášek, Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains, and Anna Goldenberg NPJ precision oncology, 2020 HTML UofT Latent-variable models for drug response prediction and genetic testing Ladislav Rampášek University of Toronto, 2020 HTML 2019 Bioinformatics Dr.VAE: improving drug response prediction via modeling of drug perturbation effects Ladislav Rampášek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, and Anna Goldenberg Bioinformatics, 2019 HTML Code 2018 Cell Learning from everyday images enables expert-like diagnosis of retinal diseases Ladislav Rampášek, and Anna Goldenberg Cell, 2018 HTML 2016 Bioinformatics Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction Aryan Arbabi, Ladislav Rampášek, and Michael Brudno Bioinformatics, 2016 HTML Code BMC Bioinf. RNA motif search with data-driven element ordering Ladislav Rampášek, Randi M Jimenez, Andrej Lupták, Tomáš Vinař, and Broňa Brejová BMC Bioinformatics, 2016 HTML Code Cell TensorFlow: biology’s gateway to deep learning? Ladislav Rampášek, and Anna Goldenberg Cell systems, 2016 HTML 2014 Bioinformatics Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing Ladislav Rampášek, Aryan Arbabi, and Michael Brudno Bioinformatics, 2014 HTML Code