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Consider the following network in Figure 3: Figure 3 Example network to compute the conditional probability.
Open image in new window Fig. 7 Example network to illustrate sequence-based classification algorithm.
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The main purpose of the EGF/NRG1 example network is to illustrate the presented methods; thus, we tried to keep the network simple while still being biologically plausible.
It's worthwhile to mention that a similar approach is adopted in example network 2 to ensure the unique deterministic steady state.
Figure 4 shows an example network topology to describe the vulnerability of the original MR-ARP scheme.
Figure 4 Example network topology to illustrate one problem of the original MR-ARP that happens when a new MR-ARP node is attached.
Figure 2 demonstrates the signal transduction behavior of a small example network according to Equation 2. For the ease of understanding, only the signals from node A, F, G, and H are presented, although signals should be propagated from each node in the network.
Experimental results verify that methods using machine learning outperform reasonable heuristic and hand-coded approaches on example networks designed to capture many of the complexities that exist in real systems.
An example network is considered to illustrate the application of the proposed framework.
Numerical results obtained from an example network are presented to demonstrate the effectiveness of the model and the solution method.
"At Finsbury Park, for example, Network Rail are going to install lifts to their platforms so we are going to put a lift in while they are completing their works".
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com