ReviewChemical reviews2022
Glycoinformatics in the Artificial Intelligence Era.
Review in Chemical reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed.
- Glycosylation in gut-brain communication: from the intestinal barrier and microbiota to immune and neural signaling.Gut microbes · 2026Review
- Rapid glycomic analysis of serum EVs reveals altered N-glycosylation patterns in ASD.Analytical and bioanalytical chemistry · 2026Article
- Orthodox vs. Paradox: Supporting the Central Dogma With Sugar Code.Proteomics · 2026Review
- Large glycomics datasets as a tool to understand the function of glycans.Nature chemical biology · 2026Review
- Deep Learning Prediction of O-Glycopeptide Tandem Mass Spectra Enhances O-Glycoproteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Glycosylation of Extracellular Vesicles: Analytical and Translational Insights into Biomarker Discovery and Regenerative Medicine.International journal of molecular sciences · 2026Review
- The 'sugar' side of extracellular vesicle-glycome: a panorama from basic characteristics, deciphering technologies, functions, to applications.Journal of nanobiotechnology · 2026Review
- Engineered small extracellular vesicles as bioactive materials: Integrating engineering strategies for cargo loading and targeted delivery systems.Bioactive materials · 2026Review
- Understanding glycan structure and function through artificial intelligence.BBA advances · 2026Review
- Modern xenotransplantation: rewiring glycan-mediated immunogenicity via genome-glycome convergence.EBioMedicine · 2026Review
- GlyContact analyzes glycan 3D structures at scale.Nature communications · 2025Article
- Atom-level machine learning of protein-glycan interactions and cross-chiral recognition in glycobiology.Science advances · 2025Article
- Seal milk oligosaccharides rival human milk complexity and exhibit functional dynamics during lactation.Nature communications · 2025Article
- Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses.bioRxiv : the preprint server for biology · 2025Article
- The emerging landscape of brain glycosylation: from molecular complexity to therapeutic potential.Experimental & molecular medicine · 2025Review
- Biochemical Applications of Microbial Rare Glycan Biosynthesis, Recognition, and Sequencing.Biochemistry · 2025Review
- Glycomics in Human Diseases and Its Emerging Role in Biomarker Discovery.Biomedicines · 2025Review
- Advancement in Clinical Glycomics and Glycoproteomics for Congenital Disorders of Glycosylation: Progress and Challenges Ahead.Biomedicines · 2025Review
- Chemical tools to study and modulate glycan-mediated host-bacteria interactions.Current opinion in chemical biology · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.