Glycoinformatics in the Artificial Intelligence Era.
Journal
Chemical reviews
ISSN: 1520-6890
Titre abrégé: Chem Rev
Pays: United States
ID NLM: 2985134R
Informations de publication
Date de publication:
26 10 2022
26 10 2022
Historique:
pubmed:
13
8
2022
medline:
28
10
2022
entrez:
12
8
2022
Statut:
ppublish
Résumé
Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.
Identifiants
pubmed: 35961636
doi: 10.1021/acs.chemrev.2c00110
pmc: PMC9615983
doi:
Substances chimiques
Polysaccharides
0
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
15971-15988Références
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