Abstract
BackgroundMost computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.MethodsIn this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.ResultsThe proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.ConclusionsBased on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.
Highlights
Most computational algorithms mainly focus on detecting highly connected subgraphs in Protein-Protein Interaction (PPI) networks as protein complexes but ignore their inherent organization
Identification of functional modules is crucial to the understanding of the structural and functional properties of networks [6,7]
Some of them are using the graph-based clustering methods for mining functional modules [11,17,18,19,20]. These studies are mainly based on the observation that densely connected regions in the PPI networks often correspond to actual protein functional modules
Summary
Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Many of these algorithms are computationally expensive. Some of them are using the graph-based clustering methods for mining functional modules [11,17,18,19,20]. These studies are mainly based on the observation that densely connected regions in the PPI networks often correspond to actual protein functional modules. Taking advantages of the SPINs, SPIN_MCODE has outperformed the plain MCODE method
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