Dear-DIA^XMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics.

ABSTRACT: Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters betwe

SUBMITTER: He Q 

PROVIDER: S-EPMC10292580 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature