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More specifically, we use the Pearson correlation to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs.
For regression problems, we use the Pearson correlation coefficient [67].
Thus, to measure whether two nodes are moving in the same direction, we use the Pearson correlation coefficient [21].
Given two genes (i_1) and (i_2), we use the Pearson correlation coefficient to define their dissimilarity (d_{text{ gene }}(i_1,i_2)) as follows: begin{aligned} d_{text{ gene }}(i_1,i_2) = 1/2times (1 - text{ corr }(({mathbf {e}}^{i_1})^T, {mathbf {e}}^{i_2})^T)).
Here, we do not use the Pearson correlation coefficient (CC) of P and E in our evaluation.
We use the Pearson correlation coefficient (R) of two genes to measure the level of their coexpression.
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Results of the SCT were correlated the MCQ and OSCE using the Pearson correlation function.
The MEs were then correlated to relevant clinical traits using the Pearson correlation and Cox regression.
To correlate CPT values with DDI, y.CHR and HI, we used the Pearson correlation test.
Correlations across measures were tested using the Pearson correlation coefficient.
Using the Pearson correlation method, the correlations among evaluated parameters were assessed.
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