Title
Protein complex detection using interaction reliability assessment and weighted clustering coefficient.
Abstract
Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes.In this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores.The higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm.
Year
DOI
Venue
2013
10.1186/1471-2105-14-163
BMC Bioinformatics
Keywords
Field
DocType
bioinformatics,microarrays,cluster analysis,algorithms
Biology,Experimental data,Saccharomyces cerevisiae Proteins,Bioinformatics,Clustering coefficient,Spurious relationship,DNA microarray
Journal
Volume
Issue
ISSN
14
1
1471-2105
Citations 
PageRank 
References 
26
0.70
18
Authors
3
Name
Order
Citations
PageRank
Nazar Zaki113914.31
Dmitry Efimov2504.90
Jose Berengueres3545.45