Sentence examples for clusters that require from inspiring English sources

It is fundamentally important that injury epidemiological studies begin to define spatial patterns statistically to determine whether observed clustering patterns occur by chance, or if there are statistically significant clusters that require further investigation (Pfeiffer et al. 2008).

4) "Other antianemic preparations" (ATC code: B03XA): In this group diverse chemical classes (e.g. biosimilars) are clustered that require processing by experts.

3) "Drugs for treatment of hyperkalemia and hyperphosphatemia" (ATC code: V03AE): In this group various agents with differing mechanisms of action are clustered that require manual processing (e.g. calcium carbonate → calcium diacetate).

Our analyses revealed an unexpected small gene cluster that requires IL-6 stimulus for early activation.

Unlike conventional clustering that requires measuring multivariate data at the same time points to calculate Euclidean 'distances' between observations, functional clustering can derive a broader class of distance measures even if the original measurements are not time-aligned among sampling units, as is common in public health applications.

For the comparison of different similarity measures, we selected four top clustering methods that require a similarity measure as input.

Methods used to cluster DNA sequences into MOTUs fall into three categories: (1) tree-based, unsupervised (non-parametric) methods [51] [53], (2) parametric methods that assume the choice of a threshold in sequence divergence prior to the clustering procedure and that require global sequence alignments [17], (3) alignment-free parametric clustering methods [54], [55].

Either the MW or the KS distance matrix for one dimension then served as input for a clustering algorithm that required no predefined specifications on cluster number and size [ 26].

In contrast to other more complex cis-regulatory module scanners that require clusters of tightly co-expressed genes and/or sets of orthologous genes [ 38, 50], the algorithm performed well in noisy, unclustered datasets.

Finally, the classification of disease patterns is described by using statistical techniques [e.g. factor or cluster analysis (11)] that require specific assumptions of the data which cannot always be met.

PCA is an unsupervised clustering method that requires little prior knowledge of the data set and acts to reduce the dimensionality of multivariate data without losing important information.