Sentence examples for time series module from inspiring English sources

Exact length cross-spectral Fourier analysis was performed using the Time Series module of Statistica (v6.1; Statsoft Inc., Tulsa, Oklahoma, USA), with a taper of 15% and a Hamming window of width five to estimate the spectral densities.

Finally, MetaboAnalyst (http://metaboanalyst.ca/) was used for statistical and enrichment analysis, applying both the statistical analysis and time series analysis modules [ 24].

Figure 1 shows BugMaps-Granger's architecture, which includes four modules: model extraction, time series creation, Granger Test module, and visualization module.

In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling.

To quantify the improvement in the smoothness of the CGM time series thanks to the denoising module, we resorted to the energy of the second order differences (ESOD) of the CGM time series, a quantity widely used in the smoothing and regularization signal-processing literature to evaluate the smoothness of a profile (26, 34).

To create the time series of source code metrics, this module receives as input the models extracted by the previous module.

The denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.

For example, in the case of a module containing genes significantly co-expressed across three conditions, significant dependent co-expression is likely to be picked up for each pair of these time series as well, yielding another three modules.

By combining current transcription factor binding site (TFBS) [ 19, 20], mutant [ 20], ChIP-chip [ 21], and heat shock time series gene expression data [ 18], we developed a module inference method, called Heat-Inducible Module Identification Algorithm (HIMIA), to reconstruct heat-inducible GRMs in yeast (see Figure 1).

The gene membership of the module spanning more conditions is compared with that of modules spanning subsets of these time series.

It was found that ery-containing module 12 became obviously correlated to erythromycin production when the time series of production lagged by 3 hours to that of gene expression, whereas module 17 partly lost its correlation to erythromycin production.