Sentence examples for cluster output from inspiring English sources

This variable quantifies the cluster output (Landry et al. 2006; Toole and Czarnitzki 2009, 2010).

This model represents the relations between the KECs (inputs) and their corresponding cluster (output).

This variable represents both a cluster output factor (Jaffe 1989) and an input factor for prototypes, products (Toole and Czarnitzki 2009, 2010), and papers (Geuna and Nesta 2006; Meyer 2006; Azoulay et al. 2009).

Pressing the 'Run Cluster' button at the bottom of the page starts the calculation process, resulting in three cluster output files that contain detailed statistical information.

Input: : Input dataset : parameters of the initial K0 component Gaussian mixture model Kmax: upper bound on maximum number of Gaussians fit to an initial cluster Output: : parameters of the refined Gaussian mixture model : refined number of Gaussians ← 0 for i = 1 to K0 do ← set of observations in associated with the ith initial Gaussian cluster.

Cluster output (Fig.  1) includes a 5 year timeline and details of the cluster with number of cases, start and end date of estimated conception, "expected" number of cases during the time period of the cluster (i.e. the average no. cases per day multiplied by the number of days), lambda (the scan statistics), and probability.

visHTS generates an interactive and visually intuitive color-coded bullseye plot that enables users to browse the cluster outputs and identify co-regulated proteins.

Cluster outputs for Erlotinib-, inhibitorble inhibitor and Cetuximab-sensitive versus resistant cell lines are presented in Additional file 8.

Clusters with a high correlation coefficient between cluster outputs and the outputs of their corresponding endophenotypes were considered influential to their corresponding endophenotype (Fig. 1, second column, clusters containing genes highlighted in purple).

Although cluster analyses often provide useful insights into the data, biological interpretation of the results is recommended, since alternative algorithms generally produce different cluster outputs and no single clustering algorithm is best suited for clustering genes into functional groups for all data sets [ 43].

Table 5 presents the GAFCM clustering output with respect to the FPI and NCE values.