Sentence examples for robustness selection from inspiring English sources

While we have a clear body of theory and predictions, until now it has been difficult to obtain measures of genetic robustness, environmental robustness, selection intensity and network connectivity for a large number of traits in a single species.

In contrast to environmental robustness, selection on genetic robustness for a trait is expected to be largely independent of the fitness cost of altering the trait and instead should correlate with the standing genetic variation for the trait that can potentially be buffered.

In the current work, it was desired to improve the robustness of selection of stable, high-affinity Fn3 domains.

A critical molecular mechanism to ensure this robustness is selection for clusters of functional binding sites (Berman et al. 2002; Berman et al. 2004), as evidenced by the evolutionary conservation of TFBSs at enhancers in the BX-C (Ho et al. 2009; Starr et al. 2011) and at the even-skipped gene (Crocker and Erives 2008; Hare et al. 2008b; Ludwig et al. 1998) in divergent insect species.

The experimental results showed that the proposed method can not only select the most featured wavelengths, but also can improve the stability and robustness of variable selection results.

Next, let us consider explicitly what it means that modularity and robustness evolve by selection.

Another example for evolution of robustness under stabilizing selection was already discussed above (Hayden et al. 2011).

A common segmentation benchmark was used to evaluate the output quality and to demonstrate its robustness concerning parameter selection.

In this section, we propose a future mobile network architecture taking into account both the flexibility of OpenFlow and the robustness of attractor selection.

To assess the robustness of positive selection, SLAC and FEL analyses were also carried out (for details, see material and methods).

Similar variable selections (which may differ only by hyperparameter) for subsamples demonstrate model selection robustness.