Ladislav Rampášek

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i10-index	17	15

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Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of TorontoVerified email at utoronto.ca
Guy WolfUniversité de Montréal; MilaVerified email at umontreal.ca
Dominique BeainiMachine Learning Researcher, InVivo AIVerified email at polymtl.ca
Benjamin Haibe-KainsScientist (Principal Investigator), Princess Margaret Cancer Centre, University Health NetworkVerified email at uhnresearch.ca
Mikhail GalkinGoogleVerified email at google.com
Petr SmirnovPostdoctoral Fellow, EMBLVerified email at embl.de
Vijay Prakash DwivediPostdoc, Stanford UniversityVerified email at stanford.edu
Zhaleh SafikhaniPost doctoral fellowVerified email at utoronto.ca
Andrej LuptákUniversity of California, IrvineVerified email at uci.edu
Broňa BrejováDept. of Computer Science, Comenius University in Bratislava, SlovakiaVerified email at fmph.uniba.sk
Renming LiuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Semih CantürkUniversity of Montreal, Mila - Quebec AI InstituteVerified email at mila.quebec
Luis MüllerPhD Student, RWTH Aaachen UniversityVerified email at cs.rwth-aachen.de
Christopher MorrisRWTH Aachen UniversityVerified email at cs.rwth-aachen.de
Michael BrudnoProfessor of Computer Science, University of Toronto; Chief Data Scientist, UHNVerified email at cs.toronto.edu
Aryan ArbabiUniversity of TorontoVerified email at cs.toronto.edu
Chun-Hao ChangMetaVerified email at cs.toronto.edu
Tomas VinarComenius University in BratislavaVerified email at fmph.uniba.sk
Misko DzambaVerified email at cs.toronto.edu

Ladislav Rampášek

Research Scientist, Isomorphic Labs

Verified email at isomorphiclabs.com - Homepage

machine learning geometric deep learning computational biology drug discovery


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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	1140	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	517	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	310	2020
TensorFlow: biology’s gateway to deep learning? L Rampasek, A Goldenberg Cell systems 2 (1), 12-14, 2016	294	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	202	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	160	2019
Attending to graph transformers L Müller, M Galkin, C Morris, L Rampášek arXiv preprint arXiv:2302.04181, 2023	92	2023
Dropout feature ranking for deep learning models CH Chang, L Rampasek, A Goldenberg arXiv preprint arXiv:1712.08645, 2017	65	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	25	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	17	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	16	2022
Hierarchical graph neural nets can capture long-range interactions L Rampášek, G Wolf arXiv preprint arXiv:2107.07432, 2021	13	2021
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
Graph positional and structural encoder S Cantürk, R Liu, O Lapointe-Gagné, V Létourneau, G Wolf, D Beaini, ... arXiv preprint arXiv:2307.07107, 2023	9	2023
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	9	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	7	2023

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