Artificial intelligence in the analysis of glycosylation data-Reference-Cited by-同舟云学术

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Artificial intelligence in the analysis of glycosylation data

Published:2022-11 Issue: Volume:60 Page:108008
ISSN:0734-9750
Container-title:Biotechnology Advances
language:en
Short-container-title:Biotechnology Advances

Author:

Li Haining,Chiang Austin W.T.,Lewis Nathan E.

Funder

National Institutes of Health

Danmarks Tekniske Universitet

Novo Nordisk Fonden

Publisher

Elsevier BV

Subject

Biotechnology,Bioengineering,Applied Microbiology and Biotechnology

Reference78 articles.

1. Mass spectrometry for glycan biomarker discovery;Aizpurua-Olaizola;TrAC - Trends Anal. Chem.,2018

2. Prediction of N-linked glycosylation sites using position relative features and statistical moments;Akmal;PLoS One,2017

3. Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells;Antonakoudis;Comput. Chem. Eng.,2021

4. Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis;Bao;Nat. Commun.,2021

5. Hierarchical classification of cancers of unknown primary using multi-omics data;Bavafaye Haghighi;Cancer Inform.,2019

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1. Analysis of carbohydrates and glycoconjugates by matrix‐assisted laser desorption/ionization mass spectrometry: An update for 2021–2022;Mass Spectrometry Reviews;2024-06-24

2. Predictive modeling for ubiquitin proteins through advanced machine learning technique;Heliyon;2024-06

3. Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector;International Journal of Biological Macromolecules;2024-06

4. Kütle Spektrometresinden Glikan Mikrodizilerine: Glikomikte Analitik Tekniklere Genel Bir Bakış;Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi;2024-05-31

5. Boltzmann Model Predicts Glycan Structures from Lectin Binding;Analytical Chemistry;2024-05-09

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