ReviewBBA advances2026
Understanding glycan structure and function through artificial intelligence.
Review in BBA advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glycoinformatics has entered the artificial intelligence era. Stymied by a lack of big data, high sequence complexity, and biosynthetic dependencies, the application of machine learning to glycomics data has largely emerged this decade. In this mini-review, we explore the latest groundbreaking computational approaches applied to glycan sequencing, classifying disease risk from glycan biomarkers, and predicting protein-glycan interactions. We detail advancements in the architectures of these models, concluding that they have matured to the stage of extracting predictive information from glycan sequences. We also discuss their challenges and limitations, and how to fully reap their rewards in the future.
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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.