Skip Navigation



Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti358
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/10/2370    most recent
bti358v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Zhang, Q.
Right arrow Articles by Welsh, W. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhang, Q.
Right arrow Articles by Welsh, W. J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 31, 2004
Revised February 23, 2005
Accepted February 24, 2005

Article

Improved method for predicting {beta}-turn using support vector machine

Qidong Zhang 1, Sukjoon Yoon 1, and William J. Welsh 1*

1 Department of Pharmacology, University of Medicine & Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School & Informatics Institute of UMDNJ, 675 Hoes Lane, Piscataway, NJ 08854

* To whom correspondence should be addressed.
William J. Welsh, E-mail: welshwj{at}umdnj.edu


  Abstract

Motivation: Numerous methods for predicting {beta}-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of {beta}-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 seven-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved thus far for predicting {beta}-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 information compression and provides a predicted reliability index.

Availability: The algorithm is available via a Web server at: http://serine.umdnj.edu/~zhangq3/betaturn/.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
D. S. Wishart, D. Arndt, M. Berjanskii, A. C. Guo, Y. Shi, S. Shrivastava, J. Zhou, Y. Zhou, and G. Lin
PPT-DB: the protein property prediction and testing database
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D222 - D229.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. Poursheikhali Asgary, S. Jahandideh, P. Abdolmaleki, and A. Kazemnejad
Analysis and identification of -turn types using multinomial logistic regression and artificial neural network
Bioinformatics, December 1, 2007; 23(23): 3125 - 3130.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
C.-T. Su, C.-Y. Chen, and C.-M. Hsu
iPDA: integrated protein disorder analyzer
Nucleic Acids Res., July 13, 2007; 35(suppl_2): W465 - W472.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.