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We then used our module detection algorithm to identify modules in the simulated networks.
We applied a spectral community detection algorithm [41] to identify modules within macaque and cat cortex.
First, we identify modules in a network using simulated annealing [22].
A novel bioinformatics analysis is used to identify modules of genes associated with biological systems (bionetwork).
To see the significance of the identified modules, a popular tool, namely MCODE [46], that is developed to detect modules in a protein interaction network was utilized to identify modules in the pathogenic network.
To identify modules (communities) within each network, we apply a variant of a spectral community detection algorithm [41].
By examining the networks of these relationships, one can identify modules, sets of highly-correlated traits that have only weak correlations with traits outside of the module.
Our goal was to identify modules associated with line differences.
Segal et al. [ 22] have developed a module-network approach to identify modules that underlie tumourigenesis.
For a part of the residential care offerings it was not possible to identify modules.
The goal is to identify modules that have the largest log likelihood ratio.
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