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Soft-modeling tools do not require a priori physical or mechanistic models to provide a decomposition of the data on the time and wavelength dimensions, the only requirement being that these two (or more) dimensions are separable.
SCALEs then uses a decomposition of the data which correlates signal intensity of probes on the DNA microarray to genomic position and allows the deciphering of genomic segments enriched within the selected populations [1].
NCA is a decomposition of the data matrix E into the control strength matrix A and the TFA matrix P through minimizing the residual Γ.
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Basically both techniques can be employed for the following: a decomposition of the unprocessed monitoring data into independent principal components to project out the signal space from the noise space.
The wavelet transform provides a mathematical tool for the hierarchical decomposition of functions to obtain a projective decomposition of the data into different scales and therefore extract local information.
To test this interpretation further and facilitate global analysis of the data (see below), we conducted a spectral decomposition of the data.
A wavelet decomposition of the data provides information on the size of the ancestry blocks and, importantly, how they are distributed along the chromosomes.
Probabilistic independent component analysis (pICA) can identify networks of coactivating brain areas based on a linear decomposition of the data and, therefore, it may miss nonlinear relationships in the data [Friston, 2000].
The regression method of Knorr and co-authors amounts to a mathematical decomposition of the data matrix, equation (5), where the individual ion chromatograms are modelled explicitly with modified Gaussian function [ 51].
A further decomposition of the data in Figure 3 into 3,479 CpG island overlapping and 1,785 non-CpG island overlapping regulatory regions shows the TSS proximal GC-rich, trinucleotide repeats are situated particularly in CpG island containing regulatory regions (Additional file 1: Figure S1).
In the majority of cases of a multiplicative mode of composition, SEATS works with an additive decomposition of the data in logarithms, (hbox {log} y_{t})).
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