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In the proposed prediction model, a sample is estimated by using the linear combinations of its P past samples and Q future samples as prediction context.
Feature selection methods target at selecting a subset of genes, whereas dimension reduction methods construct a small number of representative features (sometimes referred to as "super genes" or "latent genes" in the literature) using the linear combinations of all genes.
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PR spectra were fitted using the linear combination of several Aspnes' TDFFs [11], expressed as: Δ R R = Re ∑ j = 1 n A j e − i φ j E − E gj + i Γ j − m j + f j E, (4).
Then using the linear combination method and the constraint penalty function method, a deterministic single-objective and non-constraint optimization problem is formulated in terms of a penalty function.
Motivated in part by recent advances in the hardware and sensor information processing, such as solid state transmission and digital arbitrary waveform generators, in modern radar systems, any polarization on either transmission or reception can be synthesized by using the linear combination of the H and V components.
The optimization of the four nucleosides is performed using the linear combination of atomic orbital basis SIESTA code[15, 16], implementing the density functional theory (DFT) with the generalized gradient approximation (GGA) and the function of Perdew, Burke, and Ernzerh known as PBE[17, 18].
The values of Y1 and Y2 were subsequently computed for the healthy controls using the "linear combination" rule derived for the CD patients.
Therefore, given a set of gene expression microarray data, we attempt to construct a linear regression model for each microarray to predict the expression levels for each gene using the linear combination of the gene's motif scores.
Since the multi-locus GRS in our model is constructed using the linear combination of the genetic risk alleles, weighted by their genetic effects that are estimated from the univariate models, similar to the multivariate regression model using multiple predictors, the linear combination of such effects may over fit the data and cause inflated type I error.
Therefore, we use the linear combination in the log domain, which is equivalent to the product in equation (21).
For example, Niblack [19] and Sauvola et al. [27] used the linear combination of local mean and standard deviation for the thresholds.
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