Sentence examples for causality network from inspiring English sources

(B ) Clusters ranked from more causal to more responsive in the causality network (from blue to red for clusters 1 through 8).

In order to infer the underlying signaling and transcriptional and causality network of the 13 drug-induced apoptotic genes, we used one of the best methods for network reconstruction in the literature, the Bayesian Network or Graphical Model [ 18– 22].

Recent methods allow structural reconstruction of a causality network (e.g., Neto et al., 2010 ; Hageman et al., 2011 ; Wang and van Eeuwijk, 2014 ), and can therefore be applied on each module to reveal its internal structure.

In an SEM analysis researchers estimate a causality network according to a theoretical model, linking multiple dependent or independent constructs, each measured through a number of indicators, testing theory with empirical data [ 79].

(B ) These interactions are summarized in a causal network: clusters are ranked from more causal to more responsive (from blue to red for clusters 1 through 5, placed on a turquoise arrow that depicts ranking) in the causality network.

This is the case for, for example, Bayesian networks ([28]), Dynamic Bayesian networks ([29]) or Granger causality networks ([30].

However, this is difficult to assess here, as the pattern of covariance is very complex, with many gene pairs having high zero-order correlation and known existence of circuits in the causality networks, hence our use of the correlation variability matrix, described above, as a (weaker) criterion.

This method was also compatible with the classical causality networks [ 9].

In this method, causality and network connectivity are used for the causal knowledge network with weighted vertices and weighted network connectivity for a network with weighted edges.

Detecting Hidden Causality in Network Connections.

Combing causality in these two different timescales can prove very useful in understanding how low-frequency causal networks modulate high frequency causal networks and vice versa.