| Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform J Kranjc, J Smailović, V Podpečan, M Grčar, M Žnidaršič, N Lavrač Information Processing & Management 51 (2), 187-203, 2015 | 117 | 2015 |
| Clowdflows: a cloud based scientific workflow platform J Kranjc, V Podpečan, N Lavrač Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 101 | 2012 |
| ClowdFlows: Online workflows for distributed big data mining J Kranjc, R Orač, V Podpečan, N Lavrač, M Robnik-Šikonja Future Generation Computer Systems 68, 38-58, 2017 | 80 | 2017 |
| Orange4WS environment for service-oriented data mining V Podpečan, M Zemenova, N Lavrač The Computer Journal 55 (1), 82-98, 2012 | 70 | 2012 |
| Machine learning and data mining methods for managing Parkinson’s disease D Miljkovic, D Aleksovski, V Podpečan, N Lavrač, B Malle, A Holzinger Machine learning for health informatics: state-of-the-art and future …, 2016 | 47 | 2016 |
| SegMine workflows for semantic microarray data analysis in Orange4WS V Podpečan, N Lavrač, I Mozetič, PK Novak, I Trajkovski, L Langohr, ... BMC bioinformatics 12, 1-16, 2011 | 39 | 2011 |
| Analysis of glioblastoma patients' plasma revealed the presence of microRNAs with a prognostic impact on survival and those of viral origin A Herman, K Gruden, A Blejec, V Podpečan, H Motaln, P Rožman, ... PLoS One 10 (5), e0125791, 2015 | 37 | 2015 |
| Ménage à trois: unraveling the mechanisms regulating plant–microbe–arthropod interactions K Gruden, J Lidoy, M Petek, V Podpečan, V Flors, KK Papadopoulou, ... Trends in Plant Science 25 (12), 1215-1226, 2020 | 36 | 2020 |
| Signalling network construction for modelling plant defence response D Miljkovic, T Stare, I Mozetič, V Podpečan, M Petek, K Witek, ... PLoS One 7 (12), e51822, 2012 | 34 | 2012 |
| Karst exploration: extracting terms and definitions from karst domain corpus S Pollak, A Repar, M Martinc, V Podpečan Proceedings of eLex 2019, 934-956, 2019 | 20 | 2019 |
| Semantic subgroup explanations A Vavpetič, V Podpečan, N Lavrač Journal of Intelligent Information Systems 42, 233-254, 2014 | 20 | 2014 |
| TermEnsembler: An ensemble learning approach to bilingual<? br?> term extraction and alignment A Repar, V Podpečan, A Vavpetič, N Lavrač, S Pollak Terminology 25 (1), 93-120, 2019 | 16 | 2019 |
| Conceptual representations for computational concept creation P Xiao, H Toivonen, O Gross, A Cardoso, J Correia, P Machado, P Martins, ... ACM Computing Surveys (CSUR) 52 (1), 1-33, 2019 | 16 | 2019 |
| Real-time data analysis in ClowdFlows J Kranjc, V Podpečan, N Lavrač 2013 IEEE International Conference on Big Data, 15-22, 2013 | 16 | 2013 |
| Contrasting subgroup discovery L Langohr, V Podpečan, M Petek, I Mozetič, K Gruden, N Lavrač, ... The Computer Journal 56 (3), 289-303, 2013 | 16 | 2013 |
| Interactive exploration of heterogeneous biological networks with Biomine Explorer V Podpečan, Ž Ramšak, K Gruden, H Toivonen, N Lavrač Bioinformatics 35 (24), 5385-5388, 2019 | 11 | 2019 |
| EMBEDDIA tools, datasets and challenges: Resources and hackathon contributions S Pollak, M Robnik-Šikonja, M Purver, M Boggia, R Shekhar, M Pranjić, ... Proceedings of the EACL Hackashop on News Media Content Analysis and …, 2021 | 9 | 2021 |
| Explaining subgroups through ontologies A Vavpetič, V Podpečan, S Meganck, N Lavrač PRICAI 2012: Trends in Artificial Intelligence: 12th Pacific Rim …, 2012 | 9 | 2012 |
| Representation Learning: Propositionalization and Embeddings N Lavrač, V Podpečan, M Robnik-Šikonja Springer Nature, 2021 | 8 | 2021 |
| An implementation of the Pathfinder algorithm for sparse networks and its application on text network A Vavpetič, V Batagelj, V Podpečan 12th International Multiconference Information Society, vol. A, 236-239, 2009 | 8 | 2009 |
