ArticleBioinformatics advances2025
StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: N-linked glycosylation is one of the most basic post-translational modifications (PTMs) where oligosaccharides covalently bond with Asparagine (N). These are found in the conserved regions like N-X-S or N-X-T where X can be any residue except Proline (P). Prediction of N-linked glycosylation sites has great importance as these PTMs play a vital role in many biological processes and functionalities. Experimental methods, such as mass spectrometry, for detecting N-linked glycosylation sites are very expensive. Therefore, the prediction of N-linked glycosylation sites has become an important research field. Results: In this work, we propose StackGlyEmbed, a stacking ensemble machine learning model, to computationally predict N-linked glycosylation sites. We have explored embeddings from several protein language models and built the stacking ensemble using Support Vector Machine (SVM), Extreme Gradient Boosting (XGB) and Availability and implementation: StackGlyEmbed is freely available at: https://github.com/nafcoder/StackGlyEmbed.
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Registered trials
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