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 | 5051 | 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 | 974 | 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 | 419 | 2007 |
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 | 175 | 2021 |
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 | 174 | 2002 |
MS2PIP: a tool for MS/MS peak intensity prediction S Degroeve, L Martens Bioinformatics 29 (24), 3199-3203, 2013 | 166 | 2013 |
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 | 144 | 2005 |
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 | 142 | 2005 |
Updated MS²PIP web server delivers fast and accurate MS² 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 | 104 | 2019 |
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 | 98 | 2019 |
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 | 89 | 2004 |
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 | 86 | 2015 |
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 | 78 | 2003 |
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 | 75 | 2014 |
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 | 74 | 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 | 72 | 2011 |
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 | 67 | 2007 |
MS2Rescore: data-driven rescoring dramatically boosts immunopeptide identification rates A Declercq, R Bouwmeester, A Hirschler, C Carapito, S Degroeve, ... Molecular & Cellular Proteomics 21 (8), 2022 | 66 | 2022 |
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 |
Proteome-derived peptide libraries to study the substrate specificity profiles of carboxypeptidases S Tanco, J Lorenzo, J Garcia-Pardo, S Degroeve, L Martens, FX Aviles, ... Molecular & Cellular Proteomics 12 (8), 2096-2110, 2013 | 52 | 2013 |