By Topic

A New Measurement for Evaluating Clusters in Protein Interaction 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
$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

4 Author(s)
Min Li ; Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China ; Xuehong Wu ; Jianxin Wang ; Yi Pan

Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters are identified. Even for the most popular measurements, F-measure and P- value, bias exists for evaluating the identified clusters. In this paper, we propose a new measurement, named hF-measure, to evaluate clusters more finely and distinctly. First, we defined the hierarchical consistency and the hierarchical similarity. Then, we propose a new hierarchical measurement of hF-measure by taking into account the hierarchical organization of functional annotations and the functional similarities among proteins. The new measurement hF-measure can discriminate between different types of errors which cannot be distinguished by F- measure. The experimental results based on Gene Ontology (GO) and yeast functional modules show that hF-measure evaluates clusters more accurately when compared to F-measure.

Published in:

Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on

Date of Conference:

12-15 Nov. 2011