ArticleMolecules (Basel, Switzerland)2017
Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Addressing the Problem of Lysine Glycation Prediction in Proteins via Recurrent Neural Networks.BioMed research international · 2025Article
- On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site forFrontiers in bioinformatics · 2022Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- GlyStruct: glycation prediction using structural properties of amino acid residues.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Glycation is a non-enzymatic process occurring inside or outside the host body by attaching a sugar molecule to a protein or lipid molecule. It is an important form of post-translational modification (PTM), which impairs the function and changes the characteristics of the proteins so that the identification of the glycation sites may provide some useful guidelines to understand various biological functions of proteins. In this study, we proposed an accurate prediction tool, named Glypre, for lysine glycation. Firstly, we used multiple informative features to encode the peptides. These features included the position scoring function, secondary structure, AAindex, and the composition of
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Registered trials
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