Sentence examples for sensitive to clustering from inspiring English sources

MNTD is, on the other hand, more sensitive to clustering closer to the tips of the phylogenetic tree [ 43].

To examine whether the results were sensitive to clustering of lesions within patients, we additionally reported a planned secondary analysis that was unadjusted for clustering using the χ test.

Midrange values in terms of size and cluster radius were sensitive to clustering in our part of the country, and may appropriately characterize the parameters of clusters expected during an actual outbreak.

For example, T cell activation induces a state of persistently enhanced nanoscale TCR clustering, and nanoparticles might be sensitive to clustering at this scale in a way that larger particles are not.

As compared in Figures 9 and 11, the average conformer CT CT-opt accuracy values show a larger decrease upon clustering than the average conformer ST ST-opt values, meaning that the conformer CT CT-opt values are more sensitive to clustering than the conformer ST ST-opt values.

Conceptually, these indices express mechanical inefficiency and as argued by Leclerq et al. [ 23] and Kirn et al. [ 24] are particularly sensitive to spatially clustered simultaneous shortening and stretching.

Global cluster statistics are sensitive to spatial clustering, or departures from the null hypothesis, that occur anywhere in the study area.

FCM clustering algorithm is very sensitive to the cluster centers; the performance of cluster results is poor.

This implies that, in general, while the CT CT-opt accuracy is more sensitive to the clustering than the ST ST-opt accuracy, the effect of the clustering upon the ComboT accuracy is not sensitive to the optimization type.

> -wrap-foot> Becalle all compared methods except the DBM method are not sensitive to location clustering of the variants, the power estimates of CMC, Price, MB, RB, SKAT and C-Alpha did not differ considerably in the clustered and non-clustered scenario.

They are also sensitive to secondary clustering: one example is when a set of observations is thought to come from a single normal distribution, but in fact comes from a mixture of normals with different means [5].