Sven Degroeve

发表

SpliceMachine: predicting splice sites from high-dimensional local context representations

Yvan Saeys, Bernard De Baets, Yves Van de Peer, 2005, Bioinform..

MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation

Lennart Martens, Sven Degroeve, Davy Maddelein, 2015, Nucleic Acids Res..

Combining quantitative proteomics data processing workflows for greater sensitivity

Lennart Martens, Sven Degroeve, Christophe Van Huele, 2011, Nature Methods.

A Simulated Annealing Optimization of Audio Features for Drum Classification

Marc Leman, Bernard De Baets, Jean-Pierre Martens, 2005, ISMIR.

Collecting Ground Truth Annotations for Drum Detection in Polyphonic Music

Marc Leman, Bernard De Baets, Jean-Pierre Martens, 2005, ISMIR.

Support Vector Machines for Bass and Snare Drum Recognition

Marc Leman, Bernard De Baets, Jean-Pierre Martens, 2004, GfKl.

Machine learning applications in proteomics research: How the past can boost the future

Lennart Martens, Harald Barsnes, Wout Bittremieux, 2014, Proteomics.

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

Lennart Martens, Wout Bittremieux, Jan Ramon, 2016, Expert review of proteomics.

The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Lennart Martens, Sven Degroeve, Ralf Gabriels, 2020, bioRxiv.

Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

Lennart Martens, Sven Degroeve, Ralf Gabriels, 2019 .

Front Cover: Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries

Lennart Martens, Sven Degroeve, An Staes, 2020 .

Classification of Percussive Sounds using Support Vector Machines

Marc Leman, Bernard De Baets, Jean-Pierre Martens, 2004 .

The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows

Lennart Martens, Sven Degroeve, Ralf Gabriels, 2020, Proteomics.

Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

Lennart Martens, Sven Degroeve, Ana Sílvia Ferreira Diamantino Coelho e Silva, 2018, bioRxiv.

The effect of peptide identification search algorithms on MS2-based label-free protein quantification.

Lennart Martens, Sven Degroeve, An Staes, 2012, Omics : a journal of integrative biology.

Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

Lennart Martens, Sven Degroeve, Robbin Bouwmeester, 2019, Bioinform..

MAPPI‐DAT: data management and analysis for protein‐protein interaction data from the high‐throughput MAPPIT cell microarray platform

Lennart Martens, Sven Degroeve, Jan Tavernier, 2017, Bioinform..

AN ALGORITHM FOR DETECTING AND LABELING DRUM EVENTS IN POLYPHONIC MUSIC

Bernard De Baets, Sven Degroeve, Koen Tanghe, 2005 .

Translation initiation site prediction on a genomic scale: beauty in simplicity

Yvan Saeys, Yves Van de Peer, Sven Degroeve, 2007, ISMB/ECCB.

Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction

Yvan Saeys, Yves Van de Peer, Pierre Rouzé, 2003, ECCB.

Comprehensive and empirical evaluation of machine learning algorithms for LC retention time prediction

Lennart Martens, Sven Degroeve, Robbin Bouwmeester, 2018 .

Digging into Acceptor Splice Site Prediction: An Iterative Feature Selection Approach

Yvan Saeys, Yves Van de Peer, Sven Degroeve, 2004, PKDD.

Analysis of the resolution limitations of peptide identification algorithms.

Lennart Martens, Sven Degroeve, Niklaas Colaert, 2011, Journal of proteome research.

Removing the hidden data dependency of DIA with predicted spectral libraries

Lennart Martens, Sven Degroeve, An Staes, 2019 .

Selecting Relevant Features for Splice Site Prediction by Estimation of Distribution Algorithms.

Yvan Saeys, Yves Van de Peer, Pierre Rouzé, 2002 .

Unbiased dynamic characterization of RNA-protein interactions by OOPS

Lennart Martens, Kathryn S. Lilley, Sven Degroeve, 2018, bioRxiv.

Towards a human proteomics atlas

Lennart Martens, Sven Degroeve, Niels Hulstaert, 2012, Analytical and Bioanalytical Chemistry.

Feature selection for splice site prediction: A new method using EDA-based feature ranking

Yvan Saeys, Yves Van de Peer, Pierre Rouzé, 2004, BMC Bioinformatics.

Selecting relevant features for gene structure prediction

Yvan Saeys, Yves Van de Peer, Pierre Rouzé, 2004 .

Feature subset selection for splice site prediction

Bernard De Baets, Yves Van de Peer, Pierre Rouzé, 2002, ECCB.

Feature Ranking Using an EDA-based Wrapper Approach

Yvan Saeys, Yves Van de Peer, Sven Degroeve, 2006, Towards a New Evolutionary Computation.

MS2PIP: a tool for MS/MS peak intensity prediction

Lennart Martens, Sven Degroeve, S. Degroeve, 2013, Bioinform..

Design and evaluation of a linear classification strategy for gene structural element recognition / Sven Degroeve.

Sven Degroeve, 2004 .

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Lennart Martens, Sven Degroeve, Ralf Gabriels, 2021 .

Code and data sets for "DeepLC can predict retention times for peptides that carry as-yet unseen modifications"

Lennart Martens, Sven Degroeve, Niels Hulstaert, 2021 .

Predicting tryptic cleavage from proteomics data using decision tree ensembles.

Lennart Martens, Jan Ramon, Sven Degroeve, 2013, Journal of proteome research.

Identification of Quantitative Proteomic Differences between Mycobacterium tuberculosis Lineages with Altered Virulence

Lennart Martens, Sven Degroeve, Nicola Mulder, 2016, Front. Microbiol..

Large-scale structural analysis of the core promoter in mammalian and plant genomes

Yvan Saeys, Sven Degroeve, Pierre Rouzé, 2005, Nucleic acids research.

A posteriori quality control for the curation and reuse of public proteomics data

Lennart Martens, Johannes Griss, Rolf Apweiler, 2011, Proteomics.

Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

Lennart Martens, Sven Degroeve, Ralf Gabriels, 2019, Nucleic Acids Res..

A decoy-free approach to the identification of peptides.

Lennart Martens, Michiel Stock, Bernard De Baets, 2015, Journal of proteome research.