Sentence examples for modeling process with from inspiring English sources
Multivariate Cox regression modeling was done for DFS, EFS and OS using a forward-selection stepwise modeling process (with a forward selection method with entry probability of P = .01.01
To explain the CADWE modeling process with more insight, two examples are used as the case studies.
The presented QSAR model is statistically valid and represents the biological data of all tested compounds, including a test set of 21 ligands not used in the modeling process, with very good to excellent accuracy [q2 (training set, n = 66; leave 1/3 out) = 0.84, p2 (test set, n = 21) = 0.64].
Figure 2 describes the four steps of the modeling process, with the translation case as the example.
Further work is also needed to explore the influence of design on a full covariate modeling process, with several covariates influencing the parameters.
We repeated the entire modeling process with different settings for the discrimination distance that defines clonal clusters (see details in Materials and methods) confirming the robustness of the estimated clonality rate.
Not only those interactions are context-dependent and thus cannot be understood by keeping cells in an inappropriate milieu, like that provided by 2D-cultures, but also they follow nonlinear dynamics which make impractical modeling processes with more than two variables.
If a homology model is supplied, the user may download model coordinates in PDB format and target-template alignments generated during the modeling process, along with sequence identity, DOPE score and other relevant modeling data [ 25].
This modeling process, beginning with a stochastic time-series model of observed phenomena, may also provide insight into the other types of CFC, namely, amplitude-amplitude and phase-phase cross frequency coupling.
The CG modeling process started with the construction of the spherical diamond lattice with a lattice spacing of 0.154 nm (Figure 2(a)).
The first step in the modeling process deals with the selection of an accurate set of image metrics that are used as the input data of the model.
