Ladislav Rampášek
Ladislav Rampášek
Research Scientist, Isomorphic Labs
No verified email - Homepage
Cited by
Cited by
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
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
TensorFlow: biology’s gateway to deep learning?
L Rampasek, A Goldenberg
Cell systems 2 (1), 12-14, 2016
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
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
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
Dropout feature ranking for deep learning models
CH Chang, L Rampasek, A Goldenberg
arXiv preprint arXiv:1712.08645, 2017
Attending to graph transformers
L Müller, M Galkin, C Morris, L Rampášek
arXiv preprint arXiv:2302.04181, 2023
Dr. vae: Drug response variational autoencoder
L Rampasek, D Hidru, P Smirnov, B Haibe-Kains, A Goldenberg
arXiv preprint arXiv:1706.08203, 2017
Learning from everyday images enables expert-like diagnosis of retinal diseases
L Rampasek, A Goldenberg
Cell 172 (5), 893-895, 2018
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
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
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
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
Hierarchical graph neural nets can capture long-range interactions
L Rampášek, G Wolf
arXiv preprint arXiv:2107.07432, 2021
RNA motif search with data-driven element ordering
L Rampášek, RM Jimenez, A Lupták, T Vinař, B Brejová
BMC bioinformatics 17, 1-10, 2016
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
TensorFlow: Biology’s Gateway to Deep Learning? Cell Systems, 2 (1), 12–14
L Rampasek, A Goldenberg
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
Latent-variable models for drug response prediction and genetic testing
L Rampášek
University of Toronto, 2020
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