Improved method for predicting β-turn using support vector machine
- Authors
- Zhang Q.; Yoon S.; Welsh W.J.
- Issue Date
- May-2005
- Citation
- Bioinformatics, v.21, no.10, pp 2370 - 2374
- Pages
- 5
- Journal Title
- Bioinformatics
- Volume
- 21
- Number
- 10
- Start Page
- 2370
- End Page
- 2374
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/15759
- DOI
- 10.1093/bioinformatics/bti358
- ISSN
- 1367-4803
1367-4811
- Abstract
- Motivation: Numerous methods for predicting β-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of β-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance. Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting β-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index. © The Author 2005. Published by Oxford University Press. All rights reserved.
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