ArticleChemical science2022
GlyNet: a multi-task neural network for predicting protein-glycan interactions.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Exploring glycocalyx components powering the contact between Entamoeba histolytica and bacteria during non-opsonic phagocytosis.PLoS pathogens · 2026Review
- Orthodox vs. Paradox: Supporting the Central Dogma With Sugar Code.Proteomics · 2026Review
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Understanding glycan structure and function through artificial intelligence.BBA advances · 2026Review
- Atom-level machine learning of protein-glycan interactions and cross-chiral recognition in glycobiology.Science advances · 2025Article
- Integration of RNAseq transcriptomics andChemical science · 2025Article
- Tools for structural lectinomics: From structures to lectomes.BBA advances · 2025Article
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.bioRxiv : the preprint server for biology · 2024Article
- HumanLectome, an update of UniLectin for the annotation and prediction of human lectins.Nucleic acids research · 2024Article
- Computational toolbox for the analysis of protein-glycan interactions.Beilstein journal of organic chemistry · 2024Review
- Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level.bioRxiv : the preprint server for biology · 2023Article
- Structure-based neural network protein-carbohydrate interaction predictions at the residue level.Frontiers in bioinformatics · 2023Article
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
- GlyNet: a multi-task neural network for predicting protein-glycan interactions.Chemical science · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein-glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.
Identifiers
What OpenQuestion holds
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.