Следене
Jurica Levatic
Jurica Levatic
Postdoc, Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Потвърден имейл адрес: ijs.si
Заглавие
Позовавания
Позовавания
Година
Accurate models for P-gp drug recognition induced from a cancer cell line cytotoxicity screen
J Levatic, J Curak, M Kralj, T Smuc, M Osmak, F Supek
Journal of medicinal chemistry 56 (14), 5691-5708, 2013
622013
Self-training for multi-target regression with tree ensembles
J Levatić, M Ceci, D Kocev, S Džeroski
Knowledge-based systems 123, 41-60, 2017
592017
Semi-supervised classification trees
J Levatić, M Ceci, D Kocev, S Džeroski
Journal of Intelligent Information Systems 49, 461-486, 2017
522017
The importance of the label hierarchy in hierarchical multi-label classification
J Levatić, D Kocev, S Džeroski
Journal of Intelligent Information Systems 45, 247-271, 2015
512015
Semi-supervised trees for multi-target regression
J Levatić, D Kocev, M Ceci, S Džeroski
Information Sciences 450, 109-127, 2018
482018
Mutational signatures are markers of drug sensitivity of cancer cells
J Levatić, M Salvadores, F Fuster-Tormo, F Supek
Nature communications 13 (1), 2926, 2022
402022
Semi-supervised learning for multi-target regression
J Levatic, M Ceci, D Kocev, S Dzeroski
332014
Semi-supervised learning for quantitative structure-activity modeling
J Levatić, S Džeroski, F Supek, T Šmuc
Informatica 37 (2), 2013
212013
Semi-supervised regression trees with application to QSAR modelling
J Levatić, M Ceci, T Stepišnik, S Džeroski, D Kocev
Expert Systems with Applications 158, 113569, 2020
192020
Predicting thermal power consumption of the Mars Express satellite with machine learning
M Breskvar, D Kocev, J Levatić, A Osojnik, M Petković, N Simidjievski, ...
2017 6th International conference on space mission challenges for …, 2017
182017
Machine learning for predicting thermal power consumption of the mars express spacecraft
M Petković, R Boumghar, M Breskvar, S Džeroski, D Kocev, J Levatić, ...
IEEE Aerospace and Electronic Systems Magazine 34 (7), 46-60, 2019
172019
Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity
J Levatić, K Pavić, I Perković, L Uzelac, K Ester, M Kralj, M Kaiser, ...
European journal of medicinal chemistry 146, 651-667, 2018
162018
Community structure models are improved by exploiting taxonomic rank with predictive clustering trees
J Levatić, D Kocev, M Debeljak, S Džeroski
Ecological modelling 306, 294-304, 2015
102015
Exploiting partially-labeled data in learning predictive clustering trees for multi-target regression: A case study of water quality assessment in Ireland
S Nikoloski, D Kocev, J Levatić, DP Wall, S Džeroski
Ecological Informatics 61, 101161, 2021
82021
Semi-supervised learning for structured output prediction
J Levatić
Informatica 46 (4), 2022
52022
A framework for mutational signature analysis based on DNA shape parameters
A Karolak, J Levatić, F Supek
Plos one 17 (1), e0262495, 2022
42022
Semi-supervised predictive clustering trees for (Hierarchical) multi-label classification
J Levatić, M Ceci, D Kocev, S Džeroski
arXiv preprint arXiv:2207.09237, 2022
32022
Phenotype prediction with semi-supervised classification trees
J Levatić, M Brbić, TS Perdih, D Kocev, V Vidulin, T Šmuc, F Supek, ...
New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP …, 2018
32018
The use of the label hierarchy in hierarchical multi-label classification improves performance
J Levatić, D Kocev, S Džeroski
New Frontiers in Mining Complex Patterns: Second International Workshop …, 2014
32014
The use of the label hierarchy in HMC improves performance: A case study in predicting community structure in ecology
J Levatic, D Kocev, S Dzeroski
32013
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Статии 1–20