Jurica Levatic
Jurica Levatic
Postdoc, Genome Data Science lab, Institute for Research in Biomedicine, Barcelona, Spain
Verified email at irbbarcelona.org
TitleCited byYear
Accurate models for P-gp drug recognition induced from a cancer cell line cytotoxicity screen
J Levatić, J Ćurak, M Kralj, T Šmuc, M Osmak, F Supek
Journal of medicinal chemistry 56 (14), 5691-5708, 2013
372013
The importance of the label hierarchy in hierarchical multi-label classification
J Levatić, D Kocev, S Džeroski
Journal of Intelligent Information Systems 45 (2), 247-271, 2015
222015
Self-training for multi-target regression with tree ensembles
J Levatić, M Ceci, D Kocev, S Džeroski
Knowledge-Based Systems 123, 41-60, 2017
152017
Semi-supervised learning for multi-target regression
J Levatic, M Ceci, D Kocev, S Dzeroski
132014
Semi-supervised learning for quantitative structure-activity modeling
J Levatić, S Džeroski, F Supek, T Šmuc
Informatica 37 (2), 2013
132013
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
92017
Semi-supervised trees for multi-target regression
J Levatić, D Kocev, M Ceci, S Džeroski
Information Sciences 450, 109-127, 2018
72018
Semi-supervised classification trees
J Levatić, M Ceci, D Kocev, S Džeroski
Journal of Intelligent Information Systems 49 (3), 461-486, 2017
72017
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
62018
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
32015
The use of the label hierarchy in hierarchical multi-label classification improves performance
J Levatić, D Kocev, S Džeroski
International Workshop on New Frontiers in Mining Complex Patterns, 162-177, 2013
32013
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
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
M Petković, R Boumghar, M Breskvar, S Džeroski, D Kocev, J Levatić, ...
arXiv preprint arXiv:1809.00542, 2018
12018
Phenotype prediction with semi-supervised learning
J Levatic, M Brbic, T Perdih, D Kocev, V Vidulin, T Šmuc, F Supek, ...
Proceedings of the New Frontiers in Mining Complex Patterns: Sixth Edition …, 2017
12017
QSAR based synthesis of novel primaquine ureidoamides
K Pavić, J Levatić, F Supek, B Zorc
25th Croatian Meeting of Chemists and Chemical Engineers, 2017
12017
Phenotype Prediction with Semi-supervised Classification Trees
J Levatić, M Brbić, TS Perdih, D Kocev, V Vidulin, T Šmuc, F Supek, ...
International Workshop on New Frontiers in Mining Complex Patterns, 138-150, 2017
2017
Semi-supervised Learning for Structred Output Prediction: Doctoral Dissertation
J Levatić
J. Levatić, 2017
2017
Antimalarial screening of primaquine derivatives against erythrocytic stage of P. falciparum
B Zorc, K Pavić, F Supek, J Levatić, M Kaiser
10th Joint Meeting on Medicinal Chemistry, 2017
2017
SEMI-SUPERVISED LEARNING IN DIVERSE QUANTITATIVE STRUCTURE-ACTIVITY MODELING PROBLEMS
J Levatić, S Džeroski, F Supek, T Šmuc
INFORMACIJSKA DRUŽBA− IS 2012, 2012
2012
Improving QSAR models by exploiting unlabeled data from public databases of bioactive drug-like molecules
J Levatić, F Supek, S Džeroski
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Articles 1–20