Cart (Loading....) | Create Account
Close category search window
 

Protein Secondary Structure Prediction Using SVM with Bayesian Method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wen Yuan Liu ; Coll. of Inf. Sci. & Eng., Yanshan Univ., Qinhuangdao ; Shui Xing Wang ; Bao Wen Wang ; Jia Xin Yu

Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). In the paper, a two stage predictor is constructed to predict protein secondary structures. The first stage consists of one predictor based on the support vector machine. Bayesian discrimination is used at the second stage by considering the predicted labels of neighbor residues. The improvement of prediction performances exploits that residues tend to form structures cluster. This method outperforms the predictors based on SVM algorithm alone. Our proposed approach is promising which can be verified by its better prediction performance based on a non-redundant data set.

Published in:

Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on

Date of Conference:

16-18 May 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.