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Our results demonstrated that the modules are robust.
Furthermore, GBM alterations cluster within non-random modules, and the modules are robust across different studies; and a network-based approach can automatically identify and extract biologically relevant GBM modules, which correspond closely to prior known GBM biology.
Thus, modules are robust to the lack of signal for the plasmid genes in the DE442 data.
That the pharmacodynamic response module can achieve validation targets for both experimental contexts demonstrates that the modules are robust to changes in analog-to-time mappings.
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Furthermore, we showed that the core module is robust to parameter variation: parameter values were assumed to be given by the formula, where is normally distributed random function with a mean value of zero and half width θ.
Therefore, a synthetic design principle can either be to construct modules that are robust to noise or to explicitly use the stochastic nature of the cellular environment in the design process.
It is worth noting that for low and moderate noise levels, the fidelity does not vary substantially with changes in the branch cut height, indicating that module eigengenes are robust to inclusion/exclusion of moderate numbers of genes in the module.
Since some modules are more robust than others in terms of their average correlation coefficients (Figure 3C) the impact of a given set of experimental perturbations may not be even across the landscape.
The results of signed WGCNA are robust as it identifies similar modules in independently published data sets.
Finally, the retrieval results obtained by our method are robust with regard to the number of modules and submodules.
The static modules of proteins may be a buffer in the variation of the PPI network, and cells having these proteins are robust [4].
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