Sven Degroeve

The paper evaluates non-linear models for peptide-spectrum match rescoring in proteomics, finding that complex models increase PSMs but introduce biased FDR estimates.

iDeepLC improves retention time prediction for peptides with unseen modifications by incorporating chemical structural information, outperforming previous models.

The paper uses deep learning to predict ABA-responsive gene regulation in Arabidopsis by analyzing promoter sequences and identifies motifs beyond canonical ABF binding sites.

TIMS2Rescore is a data-driven rescoring pipeline optimized for DDA-PASEF data that improves protein identification in proteomics analyses.

This study evaluates PTM predictors using protein language models, revealing a training bias from proteomics data that overestimates prediction accuracy and suggests a solution through negative sample filtering.

MS2Rescore improves immunopeptide identification rates by integrating retrained MS2PIP, DeepLC, and Percolator, achieving higher spectrum identification rates and unique peptide counts.

The paper develops SECRiFY, a method to assess secretability of protein fragments in yeast, revealing intrinsic disorder and flexibility as key factors.

DeepLC predicts retention times for modified peptides, improving identification accuracy in LC-MS workflows by handling unseen modifications.

ionbot improves peptide identification in mass spectrometry by integrating machine learning and open modification searches, increasing PSM confidence and identification rates.

The paper compares proteogenomics and open variant search approaches for detecting single amino acid variants in shotgun proteomics data using NA12878 cell line data.

The paper introduces generalized calibration to map retention time models between different liquid chromatography setups, improving prediction accuracy across setups.

The paper removes the dependency on DDA libraries in DIA by using predicted spectral libraries, making the workflow deterministic and improving standardization in clinical proteomics.

The paper shows that using MS2PIP to predict fragment ion intensities and comparing them to experimental spectra improves Percolator's ability to distinguish PSMs while controlling false discovery rates.

The paper updates the MS2PIP web server to improve prediction accuracy for various fragmentation methods and labeling techniques, while reducing computational resources.

This study evaluates seven machine learning algorithms for predicting LC retention times across 36 metabolomics datasets, finding no single algorithm is optimal for all cases and highlighting the benefits of combining models.

The paper introduces OOPS, a method to enrich RNA-binding proteins and RNA, enabling unbiased analysis of RNA-protein interactions in dynamic systems and identifying novel RBPs.