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Moreover, these inter-module mediators appear to be responsible for linking, and possibly coordinating, the activities of multiple network modules associated with diverse functional processes and cellular localizations.
An advantage of the algorithm employed in this study (ARACNE) is that unlike clustering algorithms (such as k-means, or self-organizing maps) where cistrons or genes are assigned to mutually exclusive groups, a cistron can participate in multiple network modules, thus linking them and allowing a cistron to engage in different biological functions.
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This approach resulted in six clusters of protein interaction pairs with mutually distinct correlation change patterns, where each cluster was comprised of multiple disconnected network modules (Table 4 and Additional files 5 and 6).
For example, finding the common PPI network modules in multiple cancer networks requires the combination of graph comparison and module detection algorithms [ 3].
Because the one-dimensional layout of network nodes is created on the basis of the hierarchical modular organization of the network, a quick overview of one or multiple data tracks can often reveal network modules or sub-networks with interesting patterns, e.g. modules enriched with mutated or differentially expressed genes.
Xu et al. [ 4] developed novel graph-based methods to combine multiple microarray datasets to discover co-expression network modules related to cancer disease.
In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules.
Novel treatments will include multiple drugs interacting with key interconnected components within functional network modules, each contributing a fraction of the effects of perturbations that cause disease.
Redundancy is observed at multiple levels within biochemical networks – we have already mentioned one example in that mutations within a network modules display functionally equivalent (or at least highly similar) effects.
However, since network modules can be overlapping [ 21, 22], the same protein may be assigned to multiple modules if it has multiple functions.
In this work we present methods a) for finding consensus modules across multiple networks, b) for describing the relationship between consensus modules (eigengene networks), and c) for assessing whether the relationship between consensus modules is preserved across different networks (differential eigengene network analysis).
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