That means, there is no need to apply module finding algorithms [ 59- 61] but we obtain such modules naturally by the application of C3NET.
In order to test the effect of each parameter on the performance of the different module finding algorithms, we carried out simulations in which one parameter was varied while keeping the rest at their default values.
Cooperative miRNA network were constructed by calculating targets overlap scores, and a sub-network finding algorithm was used to identify cooperative miRNA modules.
We attempted to identify coexpression modules using an alternative module-finding algorithm developed by Ogata et al. This algorithm detects coexpression modules not only by using PCC cutoff, but also by evaluating density and connectivity of networks.
The data set for the PCC was then analysed using a network-based module-finding algorithm described previously by Ogata et al. This algorithm generates coexpression modules from a given 'seed' gene in six steps.
To characterize the PPI network of the top IRES predictions in terms of its modular structure, we used the Louvain method multi-level unsupervised clustering (module-finding) algorithm [ 53] implemented in the igraph package [ 54].
By applying the module-finding algorithm to the integrated datasets of NCI-60 cancer cell lines, we found that gene expressions were driven by diverse molecular aberrations including chromosomal segments' copy number variations, gene mutations and DNA methylations, microRNA expressions, and the expressions of transcription factors.
The same methodology was recently used in [ 20], utilizing shortest-path algorithms for module finding.
Module-finding algorithms can also be applied to predict new disease genes (Chuang et al. 2007; Goh et al. 2007): clusters enriched for known cancer genes may implicate novel genes in cancer.
The two main paradigms for module finding utilize different representations of the relationships: (i) a graph is obtained by applying a global threshold to the relationship data; or (ii) a hierarchy such as a dendrogram (or tree) is produced by a clustering algorithm.
