By Topic

Protein secondary structure prediction using support vector machine with a PSSM profile and an advanced tertiary classifier

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
$33 $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

5 Author(s)
H. -J. Hu ; Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA ; P. C. Tai ; J. He ; R. Harrison
more authors

In this study, the support vector machine (SVM) is applied as a learning machine for the secondary structure prediction. As an encoding scheme for training the SVM, position-specific scoring matrix (PSSM) is adopted. To improve the prediction accuracy, three optimization processes such as encoding scheme, sliding window size and parameter optimization are performed. For the multi-class classification, the results of three one-versus-one binary classifiers (H/E, E/C and C/H) are combined using our new tertiary classifier called SVM_Represent. By applying this new tertiary classifier, the Q3 prediction accuracy reaches 89.6% on the RSI 26 dataset and 90.1% on the CB513 dataset. Also the Segment Overlap Measure (SOV) is 85.0% on the RS 126 dataset and 85.7% on the CB513 dataset. Compared with the existing best prediction methods, our new prediction algorithm improves the accuracy about 13%) in terms of Q3 and SOV, the two most commonly used accuracy measures.

Published in:

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)

Date of Conference:

8-11 Aug. 2005