ArticleBioMed research international2015
Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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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
11 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
- iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features.Methods in molecular biology (Clifton, N.J.) · 2022Article
- BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site forFrontiers in bioinformatics · 2022Article
- Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Feature Fusion Predictor for RNA Pseudouridine Sites with Particle Swarm Optimizer Based Feature Selection and Ensemble Learning Approach.Current issues in molecular biology · 2021Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.Molecular genetics and genomics : MGG · 2020Article
- 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
- Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.Molecules (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules. The identification of glycation sites in protein may provide guidelines to understand the biological function of protein glycation. In this study, we developed a computational method to predict protein glycation sites by using the support vector machine classifier. The experimental results showed that the prediction accuracy was 85.51% and an overall MCC was 0.70. Feature analysis indicated that the composition of k-spaced amino acid pairs feature contributed the most for glycation sites prediction.
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Registered trials
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