| 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 | 58 | 2013 |
| Self-training for multi-target regression with tree ensembles J Levatić, M Ceci, D Kocev, S Džeroski Knowledge-based systems 123, 41-60, 2017 | 54 | 2017 |
| 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 | 48 | 2015 |
| Semi-supervised classification trees J Levatić, M Ceci, D Kocev, S Džeroski Journal of Intelligent Information Systems 49, 461-486, 2017 | 42 | 2017 |
| Semi-supervised trees for multi-target regression J Levatić, D Kocev, M Ceci, S Džeroski Information Sciences 450, 109-127, 2018 | 38 | 2018 |
| Semi-supervised learning for multi-target regression J Levatic, M Ceci, D Kocev, S Dzeroski | 28 | 2014 |
| Semi-supervised learning for quantitative structure-activity modeling J Levatić, S Džeroski, F Supek, T Šmuc Informatica 37 (2), 2013 | 20 | 2013 |
| 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 | 17 | 2017 |
| 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 | 16 | 2022 |
| 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 | 16 | 2018 |
| 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 | 14 | 2019 |
| 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 | 13 | 2020 |
| 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 | 9 | 2015 |
| Semi-supervised learning for structured output prediction J Levatić Informatica 46 (4), 2022 | 5 | 2022 |
| 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 | 5 | 2021 |
| 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 | 3 | 2014 |
| 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 | 3 | 2013 |
| Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification J Levatić, M Ceci, D Kocev, S Džeroski arXiv preprint arXiv:2207.09237, 2022 | 2 | 2022 |
| 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 | 2 | 2018 |
| 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 | 2 | 2017 |
Saso DzeroskiJozef Stefan Institute, Dept. of Knowledge TechnologiesVerified email at ijs.si
