The regression equation of the designed experiment was obtained by applying four models namely linear, interactive, quadratic and cubic models which were fitted to the experimental data obtained from the design system.
The second aspect (the theoretical one) aims to offer a formal proof for the approach by applying two methods which are linear algebra and Linear Logic.
We applied two linear regressions (PLS and ridge) on AquaticTox (Figure 10) and MeltingPoint (Figure 11).
Four different methods were applied: two linear regressions using a Bayesian framework, and two machine-learning ensemble algorithms.
We therefore applied two linear regression designs to our data, each of which was optimized for a specific purpose.
MaSigPro applies two generalized linear regression steps to model gene expression in time series expression data.
Therefore, by applying linear binning, two rotation-translation invariant vectors encoding a molecule are obtained: LBA C + ( m, k, dx ) = linbin ( A C + ( m, k ), dx ) LBA C - ( m, k, dx ) = linbin ( A C - ( m, k ), dx ).
As an exploratory strategy we applied two non-linear classifiers: Multilayer Perceptron and Sequential Minimal Optimization (SMO); and two linear classifiers: Logistic regression and Multilayer Perceptron in a linear form.
Finally, by applying the linear constraints of two scatterers, we can implement TOA reconstruction.
Differentially expressed genes between two age-groups were identified by applying hierarchical linear model, using limma package in R [ 40] at a P-value ≤0.01.
