Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing. - Ladislav Rampásek; Aryan Arbabi; Michael Brudno (Bioinform., 2014)

Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction. - Aryan Arbabi; Ladislav Rampásek; Michael Brudno (Bioinform., 2016)

RNA motif search with data-driven element ordering. - Ladislav Rampásek; Randi M. Jimenez; Andrej Lupták; Tomás Vinar; Brona Brejová (BMC Bioinform., 2016)

Dropout Feature Ranking for Deep Learning Models. - Chun-Hao Chang; Ladislav Rampásek; Anna Goldenberg (CoRR, 2017)

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. - Ladislav Rampásek; Daniel Hidru; Petr Smirnov; Benjamin Haibe-Kains; Anna Goldenberg (Bioinform., 2019)

Latent-variable models for drug response prediction and genetic testing. - Ladislav Rampásek (2020)

Hierarchical Graph Neural Nets can Capture Long-Range Interactions. - Ladislav Rampásek; Guy Wolf (MLSP, 2021)

Towards a Taxonomy of Graph Learning Datasets. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2021)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Sarah McGuire; Xinyi Wang; Anna Little; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; Ladislav Rampásek (LoG, 2022)

Long Range Graph Benchmark. - Vijay Prakash Dwivedi; Ladislav Rampásek; Michael Galkin; Ali Parviz; Guy Wolf; Anh Tuan Luu; Dominique Beaini (NeurIPS, 2022)

Recipe for a General, Powerful, Scalable Graph Transformer. - Ladislav Rampásek; Michael Galkin; Vijay Prakash Dwivedi; Anh Tuan Luu; Guy Wolf; Dominique Beaini (NeurIPS, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2022)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Klaser 001; Zhiyi Li; Sam Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Ladislav Rampásek; Dominique Beaini (CoRR, 2022)

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Kläser 001; Zhiyi Li; Samuel Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Andrew W. Fitzgibbon; Shenyang Huang; Ladislav Rampásek; Dominique Beaini (Trans. Mach. Learn. Res., 2023)

Graph Positional and Structural Encoder. - Semih Cantürk; Renming Liu; Olivier Lapointe-Gagné; Vincent Létourneau; Guy Wolf; Dominique Beaini; Ladislav Rampásek (ICML, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Attending to Graph Transformers. - Luis Müller; Mikhail Galkin 001; Christopher Morris 001; Ladislav Rampásek (Trans. Mach. Learn. Res., 2024)