Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

Abstract

Gao et al. report DreamDIAXMBD, a deep learning-based tool, that can extract and score chromatogram features, improving the performance of peptide-centric DIA data analysis. In contrast to the existing tools, DreamDIAXMBD demonstrates higher numbers of precursor identifications and accurate quantification in public test data sets.