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Biomed Res Int. 2014;2014:439476. doi: 10.1155/2014/439476. Epub 2014 Apr 2.

Applied graph-mining algorithms to study biomolecular interaction networks.

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Department of Computer Science, University at Albany, 1400 Washington Avenue, Albany, NY 12222, USA.
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, College of Medicine, Omaha, NE 68198-5145, USA ; Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, Omaha, NE 68198, USA.


Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks.

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