Devon Kohler

MSstatsResponse improves detection of drug-protein interactions in chemoproteomics by using a semi-parametric statistical model that avoids fixed curve shapes, enhancing accuracy and robustness in low-replicate and low-dose settings.

MSstatsBioNet integrates differential proteomics results with biomolecular networks to enable mechanistic interpretation of biological changes.

MSstats+ improves differential protein analysis in proteomic experiments by incorporating longitudinal quality metrics to reduce false positives from poor-quality measurements.

The paper introduces a statistical framework for detecting differential post-translational modification abundance in proteomics data, showing improved fold change estimation and detection compared to existing methods.