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The present approach is an effective method for discovering coherent gene modules and protein submodules.
The dense regions in the PPI networks are prone to be related to the functional modules and protein complexes [ 10].
We find significant differences in how well the clustering algorithms perform in recapitulating functional modules and protein complexes.
We use the igraph implementation contained in the R package [ 45] to identify gene modules and protein submodules.
Importantly, clusters obtained using just the Y2H network are significantly worse using all measures in recapitulating functional modules and protein complexes.
Clustering analysis of PPI data is an effective way to predict the function modules and protein complex and, study mechanisms, diagnosis and treatment of diseases.
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This lists the module and protein distribution generated with the process search.
In our study we focus on the gene module and protein submodule detection using a fast greedy modularity optimization technique [ 18] that is efficient in the analysis of large networks.
Figure 2b further shows the expression of the core modules and the protein-protein interactions (PPIs) between their gene products.
The correctly classified proteins have the same function, which provides analysis basis for us to identify protein functional modules and predict protein function accurately.
The generalization of this reconceptualization in regard to other protein interaction modules and specific protein associations is consistent with the image of the cell as a complex adaptive macromolecular system as opposed to clockwork.
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