By Topic

Modularization of Protein Interaction Networks by Incorporating Gene Ontology Annotations

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 $31
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)
Young-Rae Cho ; Dept. of Comput. Sci. & Eng., State Univ. of New York ; Woochang Hwang ; Aidong Zhang

Recent computational analyses of protein interaction networks have attempted to understand cellular organizations, processes and functions. However, they have encountered difficulties due to unreliable interaction data and the complexity of the networks. In this paper, we propose the integration of protein interaction networks with gene ontology annotations for assessing the reliability of current protein-protein interaction data. The interaction reliability can be used for building weighted protein interaction networks. We apply an information flow-based modularization algorithm to the weighted protein interaction networks. Our experimental results show that the interaction reliability between two proteins is positively correlated to the likelihood of functional and locational associations. We finally demonstrate that our approach identifies accurate modules in the protein interaction networks with high statistical confidence with respect to biological function and cellular localization. Moreover, this algorithm outperforms our previous method (Cho et al., 2006) integrating with genetic co-expressional profiles

Published in:

Computational Intelligence and Bioinformatics and Computational Biology, 2007. CIBCB '07. IEEE Symposium on

Date of Conference:

1-5 April 2007