Vinny Davies

The paper introduces a maximum bipartite matching workflow for LC-MS/MS data acquisition to improve the identification of metabolites by optimizing fragmentation spectra collection.

TopNEXt improves data acquisition in multi-sample LC-MS/MS metabolomics by extending DDA exclusion methods across samples using a Region of Interest and intensity-based scoring system.

This study benchmarks DDA and DIA acquisition methods in metabolomics using simulations and real-world validation, showing performance differences based on co-eluting ion counts.

This paper develops new data-dependent acquisition strategies for metabolomics using an in silico framework, showing they fragment more unique ions than standard methods.

The paper introduces ViMMS, a simulator for LC-MS/MS fragmentation strategies, enabling in-silico testing and optimization of mass spectrometry acquisition methods.