Toward Automatic Inference of Glycan Linkages Using MS n and Machine Learning─Proof of Concept Using Sialic Acid Linkages

Journal article

Peer reviewed

Toward Automatic Inference of Glycan Linkages Using MS n and Machine Learning─Proof of Concept Using Sialic Acid Linkages

Xinyi Ni, Nathan B. Murray, Stephanie Archer-Hartmann, Lauren E. Pepi, Richard F. Helm, Parastoo Azadi and Pengyu Hong

Journal of the American Society for Mass Spectrometry

08/24/2023

DOI: https://doi.org/10.1021/jasms.3c00132

PMID: 37621000

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Title: Toward Automatic Inference of Glycan Linkages Using MS n and Machine Learning─Proof of Concept Using Sialic Acid Linkages
Creators: Xinyi Ni - Computer Science, Brandeis University, Waltham, Massachusetts 02453, United States
Nathan B. Murray - Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States
Stephanie Archer-Hartmann - University of Georgia
Lauren E. Pepi - University of Georgia
Richard F. Helm - Virginia Tech
Parastoo Azadi - University of Georgia
Pengyu Hong - Brandeis University
Publication Details: Journal of the American Society for Mass Spectrometry
Identifiers: 9924285973501921
Academic Unit: Michtom School of Computer Science; Benjamin and Mae Volen National Center for Complex Systems
Language: English
Resource Type: Journal article