| Decision trees for hierarchical multi-label classification C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel Machine learning 73, 185-214, 2008 | 837 | 2008 |
| 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 | 288 | 2017 |
| 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, 1-14, 2010 | 231 | 2010 |
| Decision trees for hierarchical multilabel classification: A case study in functional genomics H Blockeel, L Schietgat, J Struyf, S Džeroski, A Clare Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on …, 2006 | 193 | 2006 |
| 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 | 60 | 2013 |
| Effective feature construction by maximum common subgraph sampling L Schietgat, F Costa, J Ramon, L De Raedt Machine Learning 83, 137-161, 2011 | 33 | 2011 |
| 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 | 29 | 2018 |
| An efficiently computable graph-based metric for the classification of small molecules L Schietgat, J Ramon, M Bruynooghe, H Blockeel Discovery Science: 11th International Conference, DS 2008, Budapest, Hungary …, 2008 | 27 | 2008 |
| 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, 343-376, 2013 | 24 | 2013 |
| 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 | 19 | 2006 |
| On the complexity of haplotyping a microbial community SM Nicholls, W Aubrey, K De Grave, L Schietgat, CJ Creevey, A Clare Bioinformatics 37 (10), 1360-1366, 2021 | 18 | 2021 |
| A Q-Learning algorithm for flexible job shop scheduling in a real-world manufacturing scenario JC Palacio, YM Jiménez, L Schietgat, B Van Doninck, A Nowé Procedia CIRP 106, 227-232, 2022 | 12 | 2022 |
| Beyond global and local multi-target learning M Basgalupp, R Cerri, L Schietgat, I Triguero, C Vens Information Sciences 579, 508-524, 2021 | 12 | 2021 |
| Maximum common subgraph mining: a fast and effective approach towards feature generation L Schietgat, F Costa, J Ramon, L De Raedt Proceedings of the 7th international workshop on mining and learning with …, 2009 | 11 | 2009 |
| A Polynomial-time Metric for Outerplanar Graphs. L Schietgat, J Ramon, M Bruynooghe MLG, 2007 | 11 | 2007 |
| Predicting protein function and protein-ligand interaction with the 3D neighborhood kernel L Schietgat, T Fannes, J Ramon Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada …, 2015 | 10 | 2015 |
| Recovery of gene haplotypes from a metagenome S Nicholls, W Aubrey, A Edwards, K de Grave, S Huws, S Leander, ... | 7 | 2018 |
| Graph-based data mining for biological applications L Schietgat Ai Communications 24 (1), 95-96, 2011 | 7 | 2011 |
| 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 | 5 | 2010 |
| Probabilistic recovery of cryptic haplotypes from metagenomic data SM Nicholls, W Aubrey, K de Grave, L Schietgat, CJ Creevey, A Clare bioRxiv, 117838, 2017 | 4 | 2017 |
Celine Vensprofessor, Katholieke Universiteit Leuven在 kuleuven.be 的电子邮件经过验证
