Sentence examples for models where nodes from inspiring English sources

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Software packages facilitate the interpretation of omics data by using graph models where nodes represent bio-elements and edges are bio-interactions between these elements (Junker et al., 2006).

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The impact of mobility is also noticed in the Random Direction model where nodes only change direction when they reach the scenario boundaries, and in the Random Street Map model which, similarly to the Manhattan model, is based on the organization of the streets in a city.

The performance analysis of conventional route establishment schemes in ad hoc networks is based on over-simplified communication link models such as the binary link model where nodes can perfectly communicate within a transmission radius and nothing at all is communicated outside that radius [5].

Figure 1 shows a model where nodes maintain separate queues for each AC and packets at the head-of-line (HOL) of each queue contend for channel access using AC-specific parameters [4] which are more favorable to HP traffic than the LP traffic.

To reveal hidden direct correlations between modules, i.e correlations masked by the effect of other modules, we adopted partial correlation analysis and constructed a network based on Gaussian graphical model where nodes correspond to modules and edges indicate significant partial correlations between modules.

The graph is then simulated as a physical model, where nodes interact with each other based on their physical attributes while edges constrain their movement.

If we take the strict form of the Wilson models (where all nodes, all excitatory couplings, and all inhibitory couplings are identical) and we assume that the associated differential equations are highly idealized rate models (as Wilson does), then the derived patterns in the monkey-text experiment are always unstable.

The purpose of this study was therefore to develop a generally applicable method for incorporating activity data such as metabolic flux into modularity analysis using graph models where the nodes, rather than the edges, represent the network's functional components.

To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables.

If thread head nodes within curtain rods are allowed to remain in models where all other nodes in the same thread have infinite K, the number of models increases: models missing hexamer threads split into two models (there are 8 of these in Fig. 4) and models missing both tetramer and hexamer threads (there are 3 of these in Fig. 4) split into four models.

In our proposal, we have designed a model where the nodes' IP addresses configuration has two main phases: first, a local connection address is generated by the node that wants to take part of the network.

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