Kevin Bryson

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.

The paper introduces a method to identify metabolic switches in breast cancer transcriptomes, revealing gene clusters associated with mitochondrial and metabolic states and their clinical implications.

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 study introduces gGAN, an adversarial simulator of gene expression data that outperforms existing simulators in realism and passes a Turing test for gene expression data.