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.
Keywords: Biological information process, PPI networks, protein modules detection, subcellular information.
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