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

BioMed Research International

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

Medicine

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