Leander Schietgat
Leander Schietgat
Research & Innovation Manager, Artificial Intelligence Lab, VUB
Geverifieerd e-mailadres voor vub.be
Geciteerd door
Geciteerd door
Decision trees for hierarchical multi-label classification
C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel
Machine learning 73, 185-214, 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
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
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
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, 137-161, 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
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
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
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
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
Beyond global and local multi-target learning
M Basgalupp, R Cerri, L Schietgat, I Triguero, C Vens
Information Sciences 579, 508-524, 2021
A polynomial-time metric for outerplanar graphs
L Schietgat, J Ramon, M Bruynooghe
Benelearn 2007, Annual Machine Learning Conference of Belgium and the …, 2007
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
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
Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada …, 2015
Recovery of gene haplotypes from a metagenome
S Nicholls, W Aubrey, A Edwards, K de Grave, S Huws, S Leander, ...
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
Probabilistic recovery of cryptic haplotypes from metagenomic data
SM Nicholls, W Aubrey, K De Grave, L Schietgat, CJ Creevey, A Clare
bioRxiv, 117838, 2017
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