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Finally, by substituting all these results into the definition (4.1), we obtain the final formula for the higher-order correlation of the network.
The reliability and biological correlation of the network were further verified by experimentation.
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Compared to the random networks, the predicted correlation of the curated network is extremely significant (P-value = 4.04 × 10-23).
After comparing the median-LNS with WGCNA connectivity-based statistics, we found that median-LNS correlated best with bicor.kMEall, which is the correlation of the total network module eigengenes connectivity.
(4.4)–(4.6) provide an algorithm for evaluating numerically the correlation structure of the network.
Due to this heterogeneous structure in the swollen state two correlation lengths of the network fluctuations were observed.
The solutions obtained for Vcyc > 0 underlie a substantial increase of both the global and the regional correlation level of the network.
Prediction is based on spatiotemporal correlation of the road network.
No association between the structural and functional properties of the motor network was found, by correlating the microstructural integrity of the corpus callosum fibres with the number of functional connections (Supplemental Figure S1). Figure 4 shows the correlation of the motor network measures with clinical markers.
Figure 4(a) shows a correlation of the differential network backbone values derived from the bronchial and nasal samples in the in vivo dataset GSE16008 (smokers versus nonsmokers) computed using the NPA approach in the xenobiotic metabolism network model.
Furthermore, this could offer a likely explanation for why we observed a better correlation of the differential network backbone values between the in vitro organotypic bronchial and nasal models with shorter postexposure time and Table 1).
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