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In this study, we define the connectivity as the number of achievable connections directly or indirectly.
First, different neighbour networks can be used to define the connectivity matrix among sampling sites, rather than using the truncation distance.
A major challenge of the post-genomics era is to define the connectivity of protein phosphorylation networks.
Herein, we define the connectivity of a gene as the number of reactions it affects, as characterized by the GPR's.
Along with the neuronal heterogeneity, important topographical differences define the connectivity of PPN neurons and their functionality (Martinez-Gonzalez et al. 2011).
This approach considers the total number of correlations calculated for the entire dataset and only the partial correlation values that achieve the significance threshold for the entire dataset are retained to define the connectivity between a target and hub gene.
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Hence, these results show that the strength of connection makes an important contribution to defining the connectivity profiles of these areas, despite the high variability of the strengths of the weakest projections.
The array visualises the co-ordination sphere for each molecule and therefore defines the connectivity of a molecule (node) in the hydrogen bond network.
Then using the scale-free topology criterion [ 68], the threshold parameter that defines the connectivity between genes is chosen.
With the optimized parameters, we defined the connectivity at site j0 as a ratio of the marginal log likelihoods: where Θ′ correspond to a recombinant of Θ at site j0.
To test connectivity correlations within these modules, we define the intramodular connectivity (k.in) as the sum of all connectivity measures only with genes within the same module.
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