Comparison of Protein Complexes Predicted from PPI Networks by DPClus and Newman Clustering Algorithms

A Protein-Protein Interaction network, what we call a PPI network is considered as an important source of information for prediction of protein functions. However, it is quite difficult to analyze such networks for their complexity. We expected that if we could develop a good visualizing method for PPI networks, we could predict protein functions visually because of the close relation between protein functions and protein interactions. Previously, we proposed one, which is based on clustering concepts, by extracting clusters defined as relatively densely connected group of nodes. But the results of visualization of a network differ very much depending on the clustering algorithm. Therefore, in this paper, we compare the outcome of two different clustering algorithms, namely DPClus and Newman algorithms, by applying them to a PPI network, and point out some advantages and limitations of both.