Christoph N. Schlaffner

usiGrabber automates proteomics data curation from repositories, enabling large-scale dataset creation for machine learning, achieving comparable performance to manual curation.

The paper presents a high-quality acetylation dataset and shows that transfer learning can achieve good performance with minimal data for identifying PTMs.

The paper creates a high-confidence human methylation atlas and develops a deep learning model for methylated peptide detection.

PEPerMINT imputes missing peptide abundance values in proteomics using graph neural networks, outperforming existing methods across multiple datasets and missingness scenarios.

FLEXIQuant-LF quantifies protein modification extent in label-free proteomics data using robust regression, applied to APC/C mitosis data.

PoGo maps peptides from mass spectrometry to a reference genome, outperforming existing tools on large-scale human phosphoproteome datasets.