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Due to Hilbert transform, the method selects significant decomposed signals that will be employed for signal prediction.
The piccolo method selects significant harmonics to model the data.
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Moreover, whereas the learning method selects several significant mRNAs, all mRNAs appear at low frequency in the random graphs, as shown to Figure 5(b).
In this paper we proposed a new PLS-based method to select significant genes.
Herein, PLS is used to screen the molecular descriptors; that is, the method selects the most significant descriptors from all of the available descriptors according to the PLS fitting coefficients.
It is claimed that the method is able to select significant features more efficiently than parametric methods in cases when their distributional assumptions do not hold.
‡Adjusted ORs and 95% CIs derived by multivariate logistic regression models including all the listed variables; backward method was used to select significant variables on the 0.10 level.
Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf biochemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses.
This method selects tracks randomly.
We developed the following method to combine scores from different datasets and select significant genes.
The distributions of the values for all alignments are shown in Figure 2. To select significant structures, we applied a randomization method to obtain expected values.
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