By Topic

A comparison of two machine learning methods for protein secondary structure prediction

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

3 Author(s)

Nowadays, the best methods for protein secondary structure prediction are based on neural network and support vector machine, and both of them incorporate the information from multiple sequences alignment. However the two methods were executed on different training and testing data sets. A comparison between the two methods has been carried on here. We use the most stringent cross validation test procedure to assess the two methods on CB513, which is one of the most popular used data sets. Neural network achieved a Q3 accuracy of 74.2%, while support vector machine got Q3 of 76.6%, which was slightly better than NN.

Published in:

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on  (Volume:5 )

Date of Conference:

26-29 Aug. 2004