ArticleCell reports2021
Using graph convolutional neural networks to learn a representation for glycans.
Article in Cell reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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
26 citing papers in PubMed.
- 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
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.Journal of cheminformatics · 2026Article
- Understanding glycan structure and function through artificial intelligence.BBA advances · 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
- Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome.bioRxiv : the preprint server for biology · 2025Article
- 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
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.bioRxiv : the preprint server for biology · 2024Article
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.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
- Deep self-supervised learning for biosynthetic gene cluster detection and product classification.PLoS computational biology · 2023Article
- Glycosylation and the global virome.Molecular ecology · 2023Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
- GlyNet: a multi-task neural network for predicting protein-glycan interactions.Chemical science · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes-from protein folding to viral cell entry-yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we use glycan-focused machine learning to predict viral glycan binding, which can be used to discover viral receptors.
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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.