Jurica Levatic
Jurica Levatic
Postdoc, Genome Data Science lab, Institute for Research in Biomedicine, Barcelona, Spain
Verified email at irbbarcelona.org
Title
Cited by
Cited by
Year
Accurate models for P-gp drug recognition induced from a cancer cell line cytotoxicity screen
J Levatic, J Ćurak, M Kralj, T Šmuc, M Osmak, F Supek
Journal of medicinal chemistry 56 (14), 5691-5708, 2013
472013
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
372015
Self-training for multi-target regression with tree ensembles
J Levatić, M Ceci, D Kocev, S Džeroski
Knowledge-based systems 123, 41-60, 2017
352017
Semi-supervised classification trees
J Levatić, M Ceci, D Kocev, S Džeroski
Journal of Intelligent Information Systems 49 (3), 461-486, 2017
282017
Semi-supervised trees for multi-target regression
J Levatić, D Kocev, M Ceci, S Džeroski
Information Sciences 450, 109-127, 2018
252018
Semi-supervised learning for multi-target regression
J Levatic, M Ceci, D Kocev, S Dzeroski
212014
Semi-supervised learning for quantitative structure-activity modeling
J Levatić, S Džeroski, F Supek, T Šmuc
Informatica 37 (2), 2013
192013
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
142018
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
132017
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
92015
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
52019
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
42020
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
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
22017
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
2021
Mutational signatures are markers of drug sensitivity of cancer cells
J Levatic, M Salvadores, F Fuster-Tormo, F Supek
bioRxiv, 2021
2021
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
SEMI-SUPERVISED LEARNING IN DIVERSE QUANTITATIVE STRUCTURE-ACTIVITY MODELING PROBLEMS
J Levatić, S Džeroski, F Supek, T Šmuc
INFORMACIJSKA DRUŽBA− IS 2012, 2012
2012
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Articles 1–20