13136 / 13143 - Intelligent Data Analysis

Publications 2020

Papers in WoS Journals

MALINKA, F., F. ŽELEZNÝ, and J. KLÉMA. Finding Semantic Patterns in Omics Data Using Concept Rule Learning with an Ontology-based Refinement Operator. BioData Mining. 2020, 13(13), ISSN 1756-0381. DOI 10.1186/s13040-020-00219-6.

ROSSNER, P., et al. Gene Expression and Epigenetic Changes in Mice Following Inhalation of Copper(II) Oxide Nanoparticles. Nanomaterials. 2020, 10(3), ISSN 2079-4991. DOI 10.3390/nano10030550. Available from: https://www.mdpi.com/2079-4991/10/3/550

ROSSNEROVA, A., et al. DNA Methylation Profiles in a Group of Workers Occupationally Exposed to Nanoparticles. International Journal of Molecular Sciences. 2020, 21(7), ISSN 1661-6596. DOI 10.3390/ijms21072420.

KUNC, V. and J. KLÉMA. On Tower and Checkerboard Neural Network Architectures for Gene Expression Inference. BMC Genomics. 2020, 21(5), ISSN 1471-2164. DOI 10.1186/s12864-020-06821-6. Available from: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-06821-6

DANESHMAND, A., G. SCUTARI, and V. KUNGURTSEV. Second-Order Guarantees of Distributed Gradient Algorithms. SIAM Journal on Optimization. 2020, 30(4), 3029-3068. ISSN 1095-7189. DOI 10.1137/18M121784X. Available from: https://epubs.siam.org/doi/abs/10.1137/18M121784X

CANNELLI, L., et al. Asynchronous Parallel Algorithms for Nonconvex Optimization. Mathematical Programming. 2020, 184(1-2), 121-154. ISSN 0025-5610. DOI 10.1007/s10107-019-01408-w.

SUWARTADI, E., V. KUNGURTSEV, and J. JÄSCHKE. Fast Sensitivity-Based Economic Model Predictive Control for Degenerate Systems. Journal of Process Control. 2020, 88 54-62. ISSN 0959-1524. DOI 10.1016/j.jprocont.2020.02.006.

HRUBA, P., et al. Molecular Patterns of Isolated Tubulitis Differ from Tubulitis with Interstitial Inflammation in Early Indication Biopsies of Kidney Allografts. Scientific Reports. 2020, 10(1), ISSN 2045-2322. DOI 10.1038/s41598-020-79332-9. Available from: https://www.researchgate.net/publication/347443232_Molecular_patterns_of_isolated_tubulitis_differ_from_tubulitis_with_interstitial_inflammation_in_early_indication_biopsies_of_kidney_allografts

SZIKSZAI, K., et al. LncRNA Profiling Reveals That the Deregulation of H19, WT1-AS, TCL6, and LEF1-AS1 Is Associated with Higher-Risk Myelodysplastic Syndrome. Cancers. 2020, 12(10), ISSN 2072-6694. DOI 10.3390/cancers12102726. Available from: https://www.mdpi.com/2072-6694/12/10/2726

BERGOU, E.H., Y. DIOUANE, and V. KUNGURTSEV. Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS. 2020, 185(3), 927-944. ISSN 0022-3239. DOI 10.1007/s10957-020-01666-1.

GILL, P.E., V. KUNGURTSEV, and D.P. ROBINSON. A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIAM Journal on Optimization. 2020, 30(2), 1067-1093. ISSN 1052-6234. DOI 10.1137/19M1247425.

KUNC, Vo., et al. The Superficial Anatomical Landmarks are not Reliable for Predicting the Recurrent Branch of the Median Nerve. Surgical and Radiologic Anatomy. 2020, 42(8), 939-943. ISSN 0930-1038. DOI 10.1007/s00276-020-02475-x.

HRUSTINCOVA, A., et al. Circulating Small Noncoding RNAs Have Specific Expression Patterns in Plasma and Extracellular Vesicles in Myelodysplastic Syndromes and Are Predictive of Patient Outcome. Cells. 2020, 9(4), ISSN 2073-4409. DOI 10.3390/cells9040794. Available from: https://pubmed.ncbi.nlm.nih.gov/32224889/

HRUBÁ, P., et al. Molecular Fingerprints of Borderline Changes in Kidney Allografts Are Influenced by Donor Category. Frontiers in Immunology. 2020, 11 1-10. ISSN 1664-3224. DOI 10.3389/fimmu.2020.00423.

KUNC, V., et al. Accessory Bones of the Elbow: Prevalence, Localization and Modified Classification. Journal of Anatomy. 2020, 237(4), 618-622. ISSN 0021-8782. DOI 10.1111/joa.13233.

SIMA, M., et al. The Differential Effect of Carbon Dots on Gene Expression and DNA Methylation of Human Embryonic Lung Fibroblasts as a Function of Surface Charge and Dose. International Journal of Molecular Sciences. 2020, 21(13), 1-23. ISSN 1661-6596. DOI 10.3390/ijms21134763.

Papers in Other Journals

FACCHINEI, F., et al. Convergence Rate for Diminishing Stepsize Methods in nonconvex Constrained Optimization via Ghost Penalties. Atti della Accademia Peloritana dei Pericolanti. Classe di Scienze Fisiche, Matematiche e Naturali. 2020, 98(S2), ISSN 1825-1242. DOI 10.1478/AAPP.98S2A8. Available from: https://cab.unime.it/journals/index.php/AAPP/article/view/AAPP.98S2A8

Conference Proceedings

BREMEN, T. and O. KUŽELKA. Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI-PRICAI 2020: the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence, Yokohama, 2020-07-11/2020-07-17. International Joint Conferences on Artificial Intelligence Organization, 2020. p. 4252-4258. ISBN 978-0-9992411-6-5. DOI 10.24963/ijcai.2020/587. Available from: https://www.ijcai.org/Proceedings/2020/587

ŠÍR, G., F. ŽELEZNÝ, and O. KUŽELKA. Learning with Molecules beyond Graph Neural Networks. In: Machine Learning for Molecules Workshop @ NeurIPS 2020. virtual only, 2020-12-12. Massachusetts: OpenReview.net / University of Massachusetts, 2020. Available from: https://ml4molecules.github.io/papers2020/ML4Molecules_2020_paper_24.pdf

KUŽELKA, O., V. KUNGURTSEV, and Y. WANG. Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). In: Proceedings of the 10th International Conference on Probabilistic Graphical Models. International Conference on Probabilistic Graphical Models, Aalborg, 2020-09-23/2020-09-25. Proceedings of Machine Learning Research, 2020. p. 269-280. vol. 138. ISSN 2640-3498. Available from: http://proceedings.mlr.press/v138/kuzelka20a.html

KUŽELKA, O. Complex Markov Logic Networks: Expressivity and Liftability. In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence. The 36th Conference on Uncertainty in Artificial Intelligence, Virtual online, 2020-08-03/2020-08-06. Proceedings of Machine Learning Research, 2020. p. 749-758. ISSN 2640-3498. Available from: http://proceedings.mlr.press/v124/kuzelka20a.html

SVATOŠ, M., et al. STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. In: The proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). The 24th European Conference on Artificial Intelligence, Virtual online, 2020-08-29/2020-09-08. Oxford: IOS Press, 2020. p. 1515-1522. ISSN 0922-6389. ISBN 978-1-64368-100-9. DOI 10.3233/FAIA200259.

KUŽELKA, O. and Y. WANG. Domain-Liftability of Relational Marginal Polytopes. In: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. The Twenty Third International Conference on Artificial Intelligence and Statistics, Palermo, 2020-06-03/2020-06-05. Proceedings of Machine Learning Research, 2020. p. 2284-2291. ISSN 2640-3498. Available from: http://proceedings.mlr.press/v108/kuzelka20a.html

KUNGURTSEV, V. and J. MAREČEK. A Two-Step Pre-Processing for Semidefinite Programming. In: Proceedings of the 59th IEEE Conference on Decision and Control. 59th IEEE Conference on Decision and Control, Jeju Island, 2020-12-14/2020-12-18. Institute of Electrical and Electronics Engineers, Inc., 2020. p. 384-389. ISSN 2576-2370. ISBN 978-1-7281-7447-1. DOI 10.1109/CDC42340.2020.9304494. Available from: https://ieeexplore.ieee.org/abstract/document/9304494

ZANON, M., V. KUNGURTSEV, and S. GROS. Reinforcement Learning Based on Real-Time Iteration NMPC. In: Proceedings of the IFAC World Congress 2020. IFAC World Congress 2020, Berlín, 2020-07-11/2020-07-17. Laxenburg: IFAC, 2020. p. 5213-5218. IFAC-PapersOnLine. vol. 53. ISSN 2405-8963. DOI 10.1016/j.ifacol.2020.12.1195. Available from: https://www.sciencedirect.com/science/article/pii/S2405896320315901?via%3Dihub