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

Assessing reliability of protein-protein interactions by gene ontology integration

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)
Montanez, G.D. ; Machine Learning Dept., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Young-Rae Cho

Recent advances in genome-wide identification of protein-protein interactions (PPIs) have produced an abundance of interaction data which give an insight into functional associations among proteins. However, it is known that the PPI datasets determined by high-throughput experiments or inferred by computational methods include an extremely large number of false positives. Using Gene Ontology (GO) and its annotations, we assess reliability of the PPIs by considering the semantic similarity of interacting proteins. Protein pairs with high semantic similarity are considered highly likely to share common functions, and therefore, are more likely to interact. We analyze the performance of existing semantic similarity measures in terms of functional consistency and propose a combined method that achieves improved performance over existing methods. The semantic similarity measures are applied to identify false positive PPIs. The classification results show that the combined hybrid method has higher accuracy than the other existing measures. Furthermore, the combined hybrid classifier predicts that 59.6% of the S. cerevisiae PPIs from the BioGRID database are false positives.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 IEEE Symposium on

Date of Conference:

9-12 May 2012

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.