Femtosecond laser-induced desorption of hydrogen molecules from Ru (0001): A systematic study based on machine-learned potentials S Lindner, I Lončarić, L Vrček, M Alducin, JI Juaristi, P Saalfrank The Journal of Physical Chemistry C 127 (30), 14756-14764, 2023 | 5 | 2023 |
A step towards neural genome assembly L Vrček, P Veličković, M Šikić Learning Meets Combinatorial Algorithms @ NeurIPS 2020, 2020 | 5 | 2020 |
Learning to untangle genome assembly with graph convolutional networks L Vrček, X Bresson, T Laurent, M Schmitz, M Šikić arXiv preprint arXiv:2206.00668, 2022 | 4 | 2022 |
Reconstruction of short genomic sequences with graph convolutional networks L Vrček, X Bresson, T Laurent, M Schmitz, M Šikić 2023 46th MIPRO ICT and Electronics Convention (MIPRO), 403-409, 2023 | 2 | 2023 |
Geometric deep learning framework for de novo genome assembly L Vrček, X Bresson, T Laurent, M Schmitz, K Kawaguchi, M Šikić bioRxiv, 2024.03. 11.584353, 2024 | | 2024 |
Graph Neural Network Meets de Bruijn Genome Assembly M Simunovic, L Vrcek, M Sikic 2023 International Symposium on Image and Signal Processing and Analysis …, 2023 | | 2023 |
Deep learning approach to determining the type of long reads L Vrcek, MHH Huang, R Vaser, M Šikić International Conference on Intelligent Systems for Molecular Biology, 2020 | | 2020 |
Supervised learning approach to long read classification L Vrček, M Šikić Fourth International Workshop on Data Science Abstract Book, 71, 2019 | | 2019 |
Machine learning in solid-state physics and statistical physics L Vrček University of Zagreb. Faculty of Science. Department of Physics, 2018 | | 2018 |
Genome Sequence Reconstruction Using Gated Graph Convolutional Network L Vrček, R Vaser, T Laurent, M Sikic, X Bresson | | |