ReviewBiotechnology advances2022
Artificial intelligence in the analysis of glycosylation data.
Review in Biotechnology advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Heparan Sulfate Proteoglycans: Master Regulators of Cellular Signaling, Tissue Development, and Neural Function.Journal of neuroscience research · 2026Review
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- GlycanGT: a pretrained graph transformer framework for glycan graph representation and generative learning.Bioinformatics (Oxford, England) · 2026Article
- AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.Frontiers in pharmacology · 2026Review
- Advancing recombinant protein production in CHO cells through metabolic engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Modern xenotransplantation: rewiring glycan-mediated immunogenicity via genome-glycome convergence.EBioMedicine · 2026Review
- Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.Lupus science & medicine · 2025Article
- Advancement in Clinical Glycomics and Glycoproteomics for Congenital Disorders of Glycosylation: Progress and Challenges Ahead.Biomedicines · 2025Review
- Article
- Updates implemented in version 4 of the GlyCosmos Glycoscience Portal.Analytical and bioanalytical chemistry · 2025Article
- Article
- Predictive modeling for ubiquitin proteins through advanced machine learning technique.Heliyon · 2024Article
- Boltzmann Model Predicts Glycan Structures from Lectin Binding.Analytical chemistry · 2024Article
- Article
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.bioRxiv : the preprint server for biology · 2024Article
- A Boltzmann model predicts glycan structures from lectin binding.bioRxiv : the preprint server for biology · 2024Article
- Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions.Frontiers in molecular biosciences · 2024Article
- HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease.International journal of molecular sciences · 2023Review
- Article
- Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022.Mass spectrometry reviewsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.
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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.