| Decision trees for hierarchical multi-label classification C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel Machine learning 73 (2), 185, 2008 | 578 | 2008 |
| Predicting gene function using hierarchical multi-label decision tree ensembles L Schietgat, C Vens, J Struyf, H Blockeel, D Kocev, S Džeroski BMC bioinformatics 11 (1), 2, 2010 | 172 | 2010 |
| Decision trees for hierarchical multilabel classification: A case study in functional genomics H Blockeel, L Schietgat, J Struyf, S Džeroski, A Clare European Conference on Principles of Data Mining and Knowledge Discovery, 18-29, 2006 | 167 | 2006 |
| Predicting human olfactory perception from chemical features of odor molecules A Keller, RC Gerkin, Y Guan, A Dhurandhar, G Turu, B Szalai, ... Science 355 (6327), 820-826, 2017 | 96 | 2017 |
| Predicting tryptic cleavage from proteomics data using decision tree ensembles T Fannes, E Vandermarliere, L Schietgat, S Degroeve, L Martens, ... Journal of proteome research 12 (5), 2253-2259, 2013 | 43 | 2013 |
| Effective feature construction by maximum common subgraph sampling L Schietgat, F Costa, J Ramon, L De Raedt Machine Learning 83 (2), 137-161, 2011 | 35 | 2011 |
| An efficiently computable graph-based metric for the classification of small molecules L Schietgat, J Ramon, M Bruynooghe, H Blockeel International Conference on Discovery Science, 197-209, 2008 | 27 | 2008 |
| Hierarchical multilabel classification trees for gene function prediction H Blockeel, L Schietgat, J Struyf, A Clare, S Dzeroski Probabilistic Modeling and Machine Learning in Structural and Systems …, 2006 | 18 | 2006 |
| A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics L Schietgat, J Ramon, M Bruynooghe Annals of Mathematics and Artificial Intelligence 69 (4), 343-376, 2013 | 17 | 2013 |
| A polynomial-time metric for outerplanar graphs L Schietgat, J Ramon, M Bruynooghe Benelearn 2007, Annual Machine Learning Conference of Belgium and the …, 2007 | 13 | 2007 |
| Maximum common subgraph mining: a fast and effective approach towards feature generation L Schietgat, F Costa, J Ramon, L De Raedt 7th International Workshop on Mining and Learning with Graphs, Leuven …, 2009 | 11 | 2009 |
| A machine learning based framework to identify and classify long terminal repeat retrotransposons L Schietgat, C Vens, R Cerri, CN Fischer, E Costa, J Ramon, ... PLoS computational biology 14 (4), e1006097, 2018 | 8 | 2018 |
| Predicting protein function and protein-ligand interaction with the 3D neighborhood kernel L Schietgat, T Fannes, J Ramon International Conference on Discovery Science, 221-235, 2015 | 8 | 2015 |
| Graph-based data mining for biological applications L Schietgat Ai Communications 24 (1), 95-96, 2011 | 7 | 2011 |
| Annotating transposable elements in the genome using relational decision tree ensembles E De Paula Costa, L Schietgat, R Cerri, C Vens, CN Fischer, C Carareto, ... Online preprints 23th Conference on Inductive Logic Programming, 1-6, 2013 | 4 | 2013 |
| Predicting Gene Function using Predictive Clustering Trees C Vens, L Schietgat, J Struyf, H Blockeel, D Kocev, S Džeroski Inductive Databases and Constraint-Based Data Mining, 365-387, 2010 | 4 | 2010 |
| Recovery of gene haplotypes from a metagenome S Nicholls, W Aubrey, A Edwards, K De Grave, S Huws, S Leander, ... | 3 | 2018 |
| Decision trees for hierarchical classification of transposable elements B Zamith Santos, R Gomes Mantovani, L Schietgat, C Vens, R Cerri Proceedings of the 25th Belgian-Dutch Machine Learning Conference (Benelearn …, 2016 | 3 | 2016 |
| Probabilistic recovery of cryptic haplotypes from metagenomic data SM Nicholls, W Aubrey, K De Grave, L Schietgat, CJ Creevey, A Clare | 2 | 2017 |
| Comparing chemical fingerprints for ecotoxicology L Schietgat, B Cuissart, A Lepailleur, K De Grave, B Crémilleux, R Bureau, ... | 2 | 2013 |
Celine Vensassociate professor, Katholieke Universiteit LeuvenVerified email at kuleuven.be
