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We compared the module network with the triplets inference approach described in Markowetz et al. (2007).

Finally, we added those 'classical-known' proteins that did not map the intersection S. meliloti network but did map 'the union S. meliloti network' (see methods) to extend the module network with the only condition that these proteins have to interact with themselves or with any of the proteins present in 'the module network'.

As we are using a probabilistic model and the final regulator score is defined by a posterior probability, the scores of mRNA, miRNA and discrete regulators can all be integrated and compared on the same scale to determine the final module network with heterogeneous regulators.

Considering the two-module networks with TetR/Ptet- and LacI/Plac-based NOT gates (see Figure 1A), the parameters reported in Table 1 were used to generate data, assuming the constant CV and VAR noise models, only applied to OUTPUT1 (see Figure 1D).

To test this condition, lognormal noise with constant CV was applied to OUTPUT1 (CV of 15%, 55 % and 75) and OUTPUT2 (CV of 15%) with andOUTPUT2ion CVefficient ρ, in the twofmodule network with the TetR/Ptet-based NOT gate.

Because it is hard to define general rules to analyze the effects of noise in population-averaged input-output measurements, we performed a sensitivity analysis on the two-module network with the TetR/Ptet-based NOT gate to explore the system response for different parameter configurations and to estimate the parameters of highest impact.

Deep compositional question answering with neural module networks.

Owing to these properties, ClusterONE is especially useful for detecting modules in networks with associated confidence values.

Subsequently a bioinformatics bionetwork analysis [ 23, 31] was used to correlate gene modules and networks with mate preference behaviors observed.

A high clustering coefficient indicates that the network is organized into modules, and networks with both high clustering coefficients and small diameters are collectively called small-world networks [ 32].

A bionetwork cluster analysis was performed on the differentially expressed genes in the various brain regions as previously described [ 29, 31] to identify gene modules and networks with coordinated and interconnected relationships (i.e. connectivity) [ 38].