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Bioinformatics (Oxford, England), Vol. 22, No. 24. (15 December 2006), pp. 3106-3108, doi:10.1093/bioinformatics/btl533 Key: citeulike:984063
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MOTIVATION: Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the 'wiring' of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data.
ALgorithm for module identification in networks based on edge betweenness centrality. Method is divisive - edges with high centrality are removed one by one, and in the end some number of tightly clustered modules remain. The data can be accounted for using the edge weights which affect shortest paths and thus centrality measures
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