Protein modules detection based on subcellular information

Protein modules detection from protein-protein interaction network is the hot topic in the biological information process. In this paper, we present a rank strategy for deriving protein complex, in which both subcellular information and topological information of the network are combined. First, we locate the clusters based on the competing methods from protein-protein interaction network as candidate clusters and rank these clusters based on link density calculated from the localization matrix. Second, compared with four original methods, the experimental results demonstrate that our rank strategy can improve the performances of the four original methods and is robust to all the similarity scores. Finally, the integration of the protein co-cocalizaiton information can reduce false positive percentage, especially for the extracted protein complexes only from protein-protein interaction network. Furthermore, detailed comparison with functional annotations illustrates and certifies the efficiency of the spatial information and this strategy is indicated to be helpful to find functional modules.