Leander Schietgat
Leander Schietgat
Research & Innovation Manager, Artificial Intelligence Lab, VUB
Verified email at vub.be
Cited by
Cited by
Decision trees for hierarchical multi-label classification
C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel
Machine learning 73 (2), 185, 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), 1-14, 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
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
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
Effective feature construction by maximum common subgraph sampling
L Schietgat, F Costa, J Ramon, L De Raedt
Machine Learning 83 (2), 137-161, 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
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
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
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
A polynomial-time metric for outerplanar graphs
L Schietgat, J Ramon, M Bruynooghe
Benelearn 2007, Annual Machine Learning Conference of Belgium and the …, 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
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
Graph-based data mining for biological applications
L Schietgat
Ai Communications 24 (1), 95-96, 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
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
Recovery of gene haplotypes from a metagenome
SM Nicholls, W Aubrey, A Edwards, K De Grave, S Huws, L Schietgat, ...
BioRxiv, 223404, 2019
Probabilistic recovery of cryptic haplotypes from metagenomic data
SM Nicholls, W Aubrey, K De Grave, L Schietgat, CJ Creevey, A Clare
BioRxiv, 117838, 2017
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
Computational haplotype recovery and long-read validation identifies novel isoforms of industrially relevant enzymes from natural microbial communities
SM Nicholls, W Aubrey, A Edwards, K de Grave, S Huws, L Schietgat, ...
bioRxiv, 223404, 2017
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