Deep learning method for the prediction of glycan structures from mass spectrometry data
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41592-024-02315-5.pdf
Reference6 articles.
1. Varki, A. Biological roles of glycans. Glycobiology 1, 3–49 (2017). A review article that presents a comprehensive overview of the molecular functions of glycans.
2. Ashwood, C., Lin, C.-H., Thaysen-Andersen, M. & Packer, N. H. Discrimination of isomers of released N- and O-glycans using diagnostic product ions in negative ion PGC-LC-ESI-MS/MS. J. Am. Soc. Mass Spectrom. 29, 1194–1209 (2018). This paper presents diagnostic fragmentation patterns for distinguishing glycan isomers.
3. Watanabe, Y., Aoki-Kinoshita, K. F., Ishihama, Y. & Okuda, S. GlycoPOST realizes FAIR principles for glycomics mass spectrometry data. Nucleic Acids Res. 49, D1523–D1528 (2021). This paper reports the creation of the GlycoPOST database, which stores publicly available glycomics raw data.
4. Yu, F., Koltun, V. & Funkhouser, T. Dilated residual networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1705.09914 (2017). This preprint presents the concept of dilated residual neural networks for improved image classification.
5. van der Burgt, Y. & Wuhrer, M. The role of clinical glyco(proteo)mics in precision medicine. Mol. Cell Proteomics 22, 100565 (2023). A perspective article that details advances in the use of glycan-related biomarkers in precision medicine.
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