Andy Schneider

This study evaluates proteomics for rapid detection of antibiotic resistance and species identification of ESKAPE pathogens from clinical isolates, achieving high specificity and sensitivity.

The paper optimizes data-independent acquisition (DIA) proteomics by combining in silico libraries, advanced separation, and neural network analysis to achieve high coverage and accuracy.

The paper introduces a detergent-free proteomics sample preparation method called SPEED, which outperforms existing protocols in protein quantification and reproducibility.