Glycosylation site prediction using ensembles of Support Vector Machine classifiers

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Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences.

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13 p.: ill.

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Caragea, Cornelia; Sinapov, Jivko; Silvescu, Adrian; Dobbs, Drena & Honavar, Vasant Creation Date: Unknown.

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Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences.

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13 p.: ill.

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Abstract: Background: Glycosylation 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. Results: We 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. Conclusion: Ensembles 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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  • BMC Bioinformatics, 2007, London: BioMed Central Ltd.

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  • Publication Title: BMC Bioinformatics
  • Volume: 8
  • Issue: 438
  • Pages: 13
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • Unknown

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  • November 9, 2007

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  • Sept. 6, 2013, 3:22 p.m.

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Caragea, Cornelia; Sinapov, Jivko; Silvescu, Adrian; Dobbs, Drena & Honavar, Vasant. Glycosylation site prediction using ensembles of Support Vector Machine classifiers, article, Date Unknown; [London, United Kingdom]. (digital.library.unt.edu/ark:/67531/metadc180949/: accessed August 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.