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- W4200200468 endingPage "1043" @default.
- W4200200468 startingPage "1031" @default.
- W4200200468 abstract "Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases.While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods." @default.
- W4200200468 created "2021-12-31" @default.
- W4200200468 creator A5021874293 @default.
- W4200200468 creator A5067026878 @default.
- W4200200468 creator A5072757778 @default.
- W4200200468 date "2021-12-02" @default.
- W4200200468 modified "2023-10-13" @default.
- W4200200468 title "Deep learning approaches for data-independent acquisition proteomics" @default.
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