Florian P Bayer

TOPAS converts phosphoproteomic data into patient-specific reports for molecular tumor boards, focusing on clinically relevant signaling and therapeutic targets.

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

This study shows that high-speed mass spectrometers reduce performance differences between DDA and DIA proteomics, but current LC-MS methods still lack comprehensive proteome coverage.

The paper develops a method to identify and validate kinase-substrate relationships using phosphoproteomics data and applies it to estimate kinase activity in cancer biopsies.

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

The paper uses chemical proteomics to analyze KRAS inhibitor effects on signaling networks in cancer cells, revealing cell-specific responses and phosphorylation changes.

This study identifies ATR inhibitors as synergistic with gemcitabine in killing PDAC cells by blocking the DNA damage response pathway.

PTMNavigator visualizes differentially regulated PTMs in cellular pathways using phosphoproteomics data to enhance pathway enrichment analysis and drug response visualization.

CurveCurator assesses and classifies dose-response curve significance using a recalibrated F-statistic and provides p-values and false discovery rates for improved analysis.