Sven Degroeve
Sven Degroeve
staff scientist, VIB
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Cited by
Cited by
The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray)
GA Tuskan, S Difazio, S Jansson, J Bohlmann, I Grigoriev, U Hellsten, ...
science 313 (5793), 1596-1604, 2006
Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features
E Derelle, C Ferraz, S Rombauts, P Rouzé, AZ Worden, S Robbens, ...
Proceedings of the National Academy of Sciences 103 (31), 11647-11652, 2006
Random forests as a tool for ecohydrological distribution modelling
J Peters, B De Baets, NEC Verhoest, R Samson, S Degroeve, ...
ecological modelling 207 (2-4), 304-318, 2007
Feature subset selection for splice site prediction
S Degroeve, B De Baets, Y Van de Peer, P Rouzé
Bioinformatics 18 (suppl_2), S75-S83, 2002
Large-scale structural analysis of the core promoter in mammalian and plant genomes
K Florquin, Y Saeys, S Degroeve, P Rouze, Y Van de Peer
Nucleic acids research 33 (13), 4255-4264, 2005
SpliceMachine: predicting splice sites from high-dimensional local context representations
S Degroeve, Y Saeys, B De Baets, P Rouzé, Y Van de Peer
Bioinformatics 21 (8), 1332-1338, 2005
MS2PIP: a tool for MS/MS peak intensity prediction
S Degroeve, L Martens
Bioinformatics 29 (24), 3199-3203, 2013
DeepLC can predict retention times for peptides that carry as-yet unseen modifications
R Bouwmeester, R Gabriels, N Hulstaert, L Martens, S Degroeve
Nature methods 18 (11), 1363-1369, 2021
Feature selection for splice site prediction: a new method using EDA-based feature ranking
Y Saeys, S Degroeve, D Aeyels, P Rouzé, Y Van de Peer
BMC bioinformatics 5, 1-11, 2004
Updated MS˛PIP web server delivers fast and accurate MS˛ peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques
R Gabriels, L Martens, S Degroeve
Nucleic acids research 47 (W1), W295-W299, 2019
Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction
Y Saeys, S Degroeve, D Aeyels, Y Van de Peer, P Rouze
Bioinformatics-Oxford 19 (2), 179-188, 2003
MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation
S Degroeve, D Maddelein, L Martens
Nucleic acids research 43 (W1), W326-W330, 2015
Bioinformatics Analysis of a Saccharomyces cerevisiae N-Terminal Proteome Provides Evidence of Alternative Translation Initiation and Post-Translational N …
K Helsens, P Van Damme, S Degroeve, L Martens, T Arnesen, ...
Journal of proteome research 10 (8), 3578-3589, 2011
Analysis of the resolution limitations of peptide identification algorithms
N Colaert, S Degroeve, K Helsens, L Martens
Journal of proteome research 10 (12), 5555-5561, 2011
Comprehensive and empirical evaluation of machine learning algorithms for small molecule LC retention time prediction
R Bouwmeester, L Martens, S Degroeve
Analytical chemistry 91 (5), 3694-3703, 2019
Translation initiation site prediction on a genomic scale: beauty in simplicity
Y Saeys, T Abeel, S Degroeve, Y Van de Peer
Bioinformatics 23 (13), i418-i423, 2007
Machine learning applications in proteomics research: How the past can boost the future
P Kelchtermans, W Bittremieux, K De Grave, S Degroeve, J Ramon, ...
Proteomics 14 (4-5), 353-366, 2014
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
Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions
AS C. Silva, R Bouwmeester, L Martens, S Degroeve
Bioinformatics 35 (24), 5243-5248, 2019
An algorithm for detecting and labeling drum events in polyphonic music
K Tanghe, S Degroeve, B De Baets
Proceedings of the 1st Annual Music Information Retrieval Evaluation …, 2005
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Articles 1–20