Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics
Author:
Funder
Science and Technology Commission of Shanghai Municipality
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s42256-024-00875-x.pdf
Reference50 articles.
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3. Hu, H., Khatri, K. & Zaia, J. Algorithms and design strategies towards automated glycoproteomics analysis. Mass Spectrom. Rev. 36, 475–498 (2017).
4. Hu, H., Khatri, K., Klein, J., Leymarie, N. & Zaia, J. A review of methods for interpretation of glycopeptide tandem mass spectral data. Glycoconj. J. 33, 285–296 (2016).
5. Bojar, D. & Lisacek, F. Glycoinformatics in the artificial intelligence era. Chem. Rev. 122, 15971–15988 (2022).
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