ArticleBMC bioinformatics2007
Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed, 168 citations in OpenAlex.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment.Bioinformatics (Oxford, England) · 2025Article
- BioSemAF-BiLSTM: a protein sequence feature extraction framework based on semantic and evolutionary information.Frontiers in genetics · 2025Article
- Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model.Bioinformatics (Oxford, England) · 2024Article
- Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model.PloS one · 2024Article
- Advances in the Applications of Bioinformatics and Chemoinformatics.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- Functions of Glycosylation and Related Web Resources for Its Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction.Molecules (Basel, Switzerland) · 2021Article
- Posttranslational modifications in proteins: resources, tools and prediction methods.Database : the journal of biological databases and curation · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- A Transfer Learning-Based Approach for Lysine Propionylation Prediction.Frontiers in physiology · 2021Article
- Human carbonic anhydrases and post-translational modifications: a hidden world possibly affecting protein properties and functions.Journal of enzyme inhibition and medicinal chemistry · 2020Review
- SARS-CoV-2 ORF8 and SARS-CoV ORF8ab: Genomic Divergence and Functional Convergence.Pathogens (Basel, Switzerland) · 2020Review
- Review
- N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding.Scientific reports · 2019Article
- ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.Protein science : a publication of the Protein Society · 2019Article
- Structures of soluble rabbit neprilysin complexed with phosphoramidon or thiorphan.Acta crystallographica. Section F, Structural biology communications · 2019Article
- Positive-unlabelled learning of glycosylation sites in the human proteome.BMC bioinformatics · 2019Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundGlycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences.
resultsWe explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction.
conclusionEnsembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.
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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.