13136 / 13143 - Intelligent Data Analysis

Publikační činnost 2020

Články v časopisech WoS

MALINKA, F., F. ŽELEZNÝ a 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. Dostupné z: 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 1422-0067. DOI 10.3390/ijms21072420. Dostupné z: https://pubmed.ncbi.nlm.nih.gov/32244494/

KUNC, V. a 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. Dostupné z: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-06821-6

DANESHMAND, A., G. SCUTARI a 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. Dostupné z: 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 a 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. Dostupné z: 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. Dostupné z: https://www.mdpi.com/2072-6694/12/10/2726

BERGOU, E.H., Y. DIOUANE a 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 a 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. Dostupné z: 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 1422-0067. DOI 10.3390/ijms21134763. Dostupné z: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369946/

Články v ostatních periodikách

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. Dostupné z: https://cab.unime.it/journals/index.php/AAPP/article/view/AAPP.98S2A8

Stati ve sbornících konferencí

BREMEN, T. a 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. s. 4252-4258. ISBN 978-0-9992411-6-5. DOI 10.24963/ijcai.2020/587. Dostupné z: https://www.ijcai.org/Proceedings/2020/587

ŠÍR, G., F. ŽELEZNÝ a 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. Dostupné z: https://ml4molecules.github.io/papers2020/ML4Molecules_2020_paper_24.pdf

KUŽELKA, O., V. KUNGURTSEV a 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. s. 269-280. sv. 138. ISSN 2640-3498. Dostupné z: 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. s. 749-758. ISSN 2640-3498. Dostupné z: 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. s. 1515-1522. sv. 325. ISSN 0922-6389. ISBN 978-1-64368-100-9. DOI 10.3233/FAIA200259.

KUŽELKA, O. a 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. s. 2284-2291. ISSN 2640-3498. Dostupné z: http://proceedings.mlr.press/v108/kuzelka20a.html

KUNGURTSEV, V. a 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. s. 384-389. ISSN 2576-2370. ISBN 978-1-7281-7447-1. DOI 10.1109/CDC42340.2020.9304494. Dostupné z: https://ieeexplore.ieee.org/abstract/document/9304494

ZANON, M., V. KUNGURTSEV a 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. s. 5213-5218. IFAC-PapersOnLine. sv. 53. ISSN 2405-8963. DOI 10.1016/j.ifacol.2020.12.1195. Dostupné z: https://www.sciencedirect.com/science/article/pii/S2405896320315901?via%3Dihub