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Jurica Levatic
Jurica Levatic
Postdoc, Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Geverifieerd e-mailadres voor ijs.si
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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
642013
Self-training for multi-target regression with tree ensembles
J Levatić, M Ceci, D Kocev, S Džeroski
Knowledge-based systems 123, 41-60, 2017
622017
Semi-supervised classification trees
J Levatić, M Ceci, D Kocev, S Džeroski
Journal of Intelligent Information Systems 49, 461-486, 2017
592017
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
572022
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
532015
Semi-supervised trees for multi-target regression
J Levatić, D Kocev, M Ceci, S Džeroski
Information Sciences 450, 109-127, 2018
522018
Semi-supervised learning for multi-target regression
J Levatic, M Ceci, D Kocev, S Dzeroski
342014
Semi-supervised learning for quantitative structure-activity modeling
J Levatić, S Džeroski, F Supek, T Šmuc
Informatica 37 (2), 2013
222013
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
212020
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
192017
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
182019
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
172018
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
92021
Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification
J Levatić, M Ceci, D Kocev, S Džeroski
International Journal of Intelligent Systems, 2024
72024
Whole-genome Mutational Analysis for Tumor-informed Detection of Circulating Tumor DNA in Patients with Urothelial Carcinoma
I Nordentoft, SV Lindskrog, K Birkenkamp-Demtröder, S Gonzalez, ...
European Urology 86 (4), 301-311, 2024
6*2024
Semi-supervised learning for structured output prediction
J Levatić
Informatica 46 (4), 2022
62022
CLUSplus: A decision tree-based framework for predicting structured outputs
M Petković, J Levatić, D Kocev, M Breskvar, S Džeroski
SoftwareX 24, 101526, 2023
52023
A framework for mutational signature analysis based on DNA shape parameters
A Karolak, J Levatić, F Supek
Plos one 17 (1), e0262495, 2022
52022
Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft
M Petković, L Lucas, J Levatić, M Breskvar, T Stepišnik, A Kostovska, ...
Scientific Data 9 (1), 229, 2022
32022
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Artikelen 1–20