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On the other hand, because overlap between the modules is restrained, we expect the modules to be functionally heterogeneous and to represent relatively independent biological processes and transcriptional regulatory programs.
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The average absolute error of the power for all the modules is comprised between 0 and 5% depending on the type of the module technology.
The connections between the modules are important for load transfer.
In this diagram, the communication links between the "modules" are also shown.
Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage.
With global perturbations and high modularity each module handles its internal perturbations and, if this fails, flows between the modules are too weak for the perturbation to spread.
The relationships between the modules are deterministic, but if the inputs are given with uncertainties, the uncertainties are propagated through the model using Monte Carlo simulation and the outputs are probability distributions.
The strong interactions between the factors or the modules are crucial for understanding the model complexity.
Looking closer at these results we found that 16 of the modules were shared between the DR3/DR3 and DR3/DRX groups.
In the second type, coexpression between genes in the module is not present in the healthy tissue, but it appears in the diseased states (Fig. 1 plots C-D).
In practice, the interaction between these modules is limited.
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