Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods

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 glycati...

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Detalles Bibliográficos
Autores principales: Liu, Yan, Gu, Wenxiang, Zhang, Wenyi, Wang, Jianan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413511/
https://www.ncbi.nlm.nih.gov/pubmed/25961025
http://dx.doi.org/10.1155/2015/561547
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author Liu, Yan
Gu, Wenxiang
Zhang, Wenyi
Wang, Jianan
author_facet Liu, Yan
Gu, Wenxiang
Zhang, Wenyi
Wang, Jianan
author_sort Liu, Yan
collection PubMed
description 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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spelling pubmed-44135112015-05-10 Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods Liu, Yan Gu, Wenxiang Zhang, Wenyi Wang, Jianan Biomed Res Int Research Article 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. Hindawi Publishing Corporation 2015 2015-04-15 /pmc/articles/PMC4413511/ /pubmed/25961025 http://dx.doi.org/10.1155/2015/561547 Text en Copyright © 2015 Yan Liu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Yan
Gu, Wenxiang
Zhang, Wenyi
Wang, Jianan
Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title_full Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title_fullStr Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title_full_unstemmed Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title_short Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
title_sort predict and analyze protein glycation sites with the mrmr and ifs methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413511/
https://www.ncbi.nlm.nih.gov/pubmed/25961025
http://dx.doi.org/10.1155/2015/561547
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AT zhangwenyi predictandanalyzeproteinglycationsiteswiththemrmrandifsmethods
AT wangjianan predictandanalyzeproteinglycationsiteswiththemrmrandifsmethods

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