Hierarchical Organization of Functional Modules in Weighted Protein Interaction Networks Using Clustering Coefficient

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Several graph clustering approaches have been proposed to detect functional modules from such networks. However, all methods of predicting protein interactions are known to yield a nonnegligible amount of false positives. Most of the graph clustering algorithms are challenging to be used in the network with high false positives. We extend the protein interaction network from unweighted graph to weighted graph and propose an algorithm for hierarchically clustering in the weighted graph. The proposed algorithm HC-Wpin is applied to the protein interaction network of S.cerevisiae and the identified modules are validated by GO annotations. Many significant functional modules are detected, most of which are corresponding to the known complexes. Moreover, our algorithm HC-Wpin is faster and more accurate compared to other previous algorithms. The program is available at http://bioinfo.csu.edu.cn/limin/HC-Wpin.