By Topic

Protein Classification Using Artificial Neural Networks with Different Protein Encoding Methods

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

1 Author(s)
Andre Luis Debiaso Rossi ; State Univ. of Londrina, Londrina

The fast growth of annotated biological data implies in the need of developing new techniques and tools to classify these data, in such way that they can be useful. Protein classification is one relevant task in this context. This paper presents different models of neural network, aiming to compare the influence of the protein sequence encoding method in the performance of the Neural network to classify proteins. Besides, it is proposed two methods of protein sequence encoding, that were tested with several neural network, for classifying proteins using two approaches: based on families of proteins and based on function of proteins. The results of performance of the neural networks are presented and compared with other works in the area.

Published in:

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

Date of Conference:

20-24 Oct. 2007