By Topic

Prediction of enzyme subclass by using support vector machine based on improved parameters

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

2 Author(s)
Xiuzhen Hu ; Coll. of Sci., Inner Mongolia Univ. of Technol., Hohhot, China ; Ting Wang

By using of the improved parameters with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the enzyme subclasses of the six main functional classes is proposed. And the better results are obtained. The overall Jackknife success rates in identifying the enzyme subclasses of oxidoreductase, transferases, hydrolases, lyases, isomerases, and ligases are 94.23%, 92.94%, 90.85%, 98.43%, 99.37% and 98.96%, respectively. The results indicate that our method is helpful tool for enzyme subclasses prediction.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:1 )

Date of Conference:

26-28 July 2011