Sentence examples for steps of data transformation from inspiring English sources

However, when studying a limited number of continuous or categorical variables that are not closely correlated with each other, these steps of data transformation may not be necessary.

Learning contextual gene set interaction networks and identifying condition specificity involves several steps of data transformation, as illustrated in Figure 1, and is described further in the Methods section.

It is believed that the extracted results using nonlinear curve fitting method is more accurate due to the utilization of actual data and the minimization of data transformation steps required in the linear curve fitting [42, 43]. Figure 7 shows the J-E results for the samples annealed in O2, Ar, and FG ambient, which fitted well with FN tunneling model.

The process of data transformations to obtain the required statistics from what is reported in observational studies, consists of four steps.

The normalization consists of the following three steps: first, data transformation (set measurements less than 300.0 to 300.0); second, per chip (normalize each chip to the 50th percentile of the measurements taken from that chip); and third, per gene (normalize each gene to the median of the measurements for that gene).

In this paper, we review the system architecture and performance providing examples of output products, showing the progression of data processing steps, and data transformations that are involved.

The synthesis statements remain consistent with the review authors' work but also build in the results of the data transformation performed in earlier steps.

We retained 75 principal components of PCA in the preliminary data transformation step, which altogether contained more that 85% of the total genetic variation.

The type of the regression obtained in SLP training should be controlled by the sort of cost function as well as by training parameters (the number of iterations, learning step, etc).. Whitening data transformation prior to training the perceptron is a tool to incorporate a prior information into the prediction rule design, and helps both to diminish the generalization error and the training time.

The process of KDD consists of five major steps, namely the extraction goal, data selection, data transformation, data mining techniques, and finally, the interpretation of results, these steps are given in Fig.  1.

Stepping through the tiers of architecture results in data transformation from loosely structured (semi- or unstructured) document to rigidly structured data records.