ArticleMolecules (Basel, Switzerland)2021
Dealing with the Ambiguity of Glycan Substructure Search.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- 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
- Glycosaminoglycans: What Remains To Be Deciphered?JACS Au · 2023Review
- Review
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
- Native glycosylation and binding of the antidepressant paroxetine in a low-resolution crystal structure of human myeloperoxidase.Acta crystallographica. Section D, Structural biology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.
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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.