Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods
Joint Authors
Wang, Jia-Nan
Zhang, Wen-yi
Liu, Yan
Gu, Wenxiang
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-15
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Yan& Gu, Wenxiang& Zhang, Wen-yi& Wang, Jia-Nan. 2015. Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods. BioMed Research International،Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1055917
Modern Language Association (MLA)
Liu, Yan…[et al.]. Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods. BioMed Research International No. 2015 (2015), pp.1-6.
https://search.emarefa.net/detail/BIM-1055917
American Medical Association (AMA)
Liu, Yan& Gu, Wenxiang& Zhang, Wen-yi& Wang, Jia-Nan. Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-6.
https://search.emarefa.net/detail/BIM-1055917
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055917