By Topic

Data representation influences protein secondary structure prediction using artificial neural networks

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

3 Author(s)
Lamont, O. ; Centre of Bioinformatics & Biol. Comput., Murdoch Univ., WA, Australia ; Hiew Hong Liang ; Bellgard, M.

Artificial Neural Networks (ANN) have been used very successfully for a number of classification problems in the molecular biology field. Protein secondary structure prediction is one of the oldest and best defined of these classification problems. Yet despite the considerable amount of work conducted in this field there still remain a number of fundamental computational issues that have not been thoroughly investigated, if considered at all. One important issue is identifying an appropriate data representation for input into the ANN. In this paper, we have investigated a range of new encoding schemes and evaluated their performance using recently introduced evaluation criterion. We have done this by preserving the redundant information of DNA codons that is lost when they are translated into amino acids. Interestingly, with our new data representation, the β-strand prediction performance was consistently higher (14% improvement) over the accuracy of the ANNs trained when the conventional representation was used.

Published in:

Intelligent Information Systems Conference, The Seventh Australian and New Zealand 2001

Date of Conference:

18-21 Nov. 2001