Cart (Loading....) | Create Account
Close category search window
 

Interaction prediction of PDZ domains using a machine learning approach

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)
Kalyoncu, S. ; Chem. & Biol. Eng., Koc Univ., Istanbul, Turkey ; Keskin, O. ; Gursoy, A.

Protein interaction domains play crucial roles in many complex cellular pathways. PDZ domains are one of the most common protein interaction domains. Prediction of binding specificity of PDZ domains by a computational manner could eliminate unnecessary, time-consuming experiments. In this study, interactions of PDZ domains are predicted by using a machine learning approach in which only primary sequences of PDZ domains and peptides are used. In order to encode feature vectors for each interaction, trigram frequencies of primary sequences of PDZ domains and corresponding peptides are calculated. After construction of numerical interaction dataset, we compared different classifiers and ended up with Random Forest (RF) algorithm which gave the top performance. We obtained very high prediction accuracy (91.4%) for binary interaction prediction which outperforms all previous similar methods.

Published in:

Health Informatics and Bioinformatics (HIBIT), 2010 5th International Symposium on

Date of Conference:

20-22 April 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.