Functional module detection by functional flow pattern mining in protein interaction networks
© Cho et al; licensee BioMed Central Ltd 2008
Published: 30 October 2008
A functional module has been defined as a group of molecules that participate in the same functional activities. Various graph-theoretic or data-mining techniques have been applied to discover functional modules from protein interaction networks . However, their performance has been compromised by false-positive and false-negative interaction data and complex connectivity of the interaction networks. In our earlier study , we have introduced the functional flow-based approach to efficiently identify overlapping modules, which are generally large-sized, from interaction networks. In this abstract, we extend this approach by mining functional flow patterns for the purpose of detecting small-sized modules for specific functions.
Our approach includes three steps. First, we integrate the interaction network with semantic data from Gene Ontology  to generate a weighted interaction network, which is functionally reliable. Next, we simulate functional flow starting from selected informative proteins and identify primary modules for general-level functions . As the last step, we obtain the set of functional flow patterns for each primary module by flow simulation from all nodes within the module. A functional flow pattern is defined as a sequence of quantities of functional influence of a source protein on target proteins. The coherent patterns are then captured by a pattern-based clustering algorithm  as final modules for specific-level functions. The significant assumption is that if two source proteins have similar functional flow patterns across all the other targets proteins, then they are likely to have the same function.
ORI recognition and priming complex formation
extension and polymerization activity
DNA conformation modification
Mitotic cell cycle
number of modules
average module size
functional flow pattern
The modules, identified from protein interaction networks, provide an understanding of functional associations among proteins. In this study, we introduced a framework to detect functional modules in protein interaction networks. We demonstrated that our approach accurately handles the erroneous and complex networks.
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