In Search of Disentanglement in Tandem Mass Spectrometry Datasets.

ABSTRACT: Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and more accurate queries of large MS/MS spectra databases for metabolite identification. In this study, we apply generative modeling and representation learning using variational autoencoders to understand the extent to which tandem mass spectra can be disentangled into their factors of generation (e.g., collision energy, ionization mode, instrument type, etc.) with minimal prior knowledge of the factors. We find that variational autoencode

SUBMITTER: Abram KJ 

PROVIDER: S-EPMC10526774 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature