Sentence examples for derived clustered from inspiring English sources

A neighbor-joining phylogenetic tree constructed on the basis of the helicase gene revealed that all the amplicons we derived clustered with mimivirus isolates; however, according to the sequences alignment analysis, 3 of them clustered directly with the Megavirus chilensis group.

As explained in the Methods, Affymetrix software plots RAS1 scores against RAS2 scores and uses empirically derived clustering information to trichotomise these RAS scores as genotypes for DNA of an individual.

The derived cluster density (CD) as a function of fractional intensity remaining after each round of photobleaching (p) is given by Eq. (1).

The cluster node expansion and cluster edge expansion measures have been considered in [24] for growing a cluster using a derived cluster node expansion capability measure.

The cluster node expansion and cluster edge expansion measures are considered for growing a cluster using a derived cluster node expansion capability measure.

First, the integrated SZE flux is compared with X-ray derived gas mass, spectroscopic temperature, and total mass to determine how firmly connections between observables and derived cluster properties can be established in SZE surveys.

We propose a two-step general framework, Clustering Embedded Network Inference (CENI), to efficiently incorporate the information from derived clusters to infer information networks.

Higher dimension potentially provides the model more description power of the observed cascade data, which yields the derived clusters better help the network inference.

A cluster node expansion and a cluster edge expansion measure are considered in our paper for growing a cluster spanning a hierarchy of sub-topic sub-goal clusters using a derived cluster node expansion capability measure.

In the following sections, the various morphometric parameters and their implications are discussed for the entire watershed and the sub-watersheds based on the derived cluster groups (Fig. 3).

The k-medians algorithm uses the median instead of the mean as the centre of the derived clusters and is therefore better in dealing with outliers in the data.