The linear predictor represents the conditional expectation function, and it is structured as η = 1 μ + ∑ j = 1 J X j β j + ∑ l = 1 L u l, (1 where μ is an intercept, X j are design matrices for predictors, X j = { x ijk}, β j are vectors of effects associated to the columns of X j, and u l = { u l1, …, u ln} are vectors of random effects.
The full model presented in (2) contains a predictor representing the average effect of experimental condition j plus an additional parameter for each parametric regressor k considered.
When model 2 extends model 1 through added predictors, represents the incremental predictive ability of the added predictors, and heterogeneity in depends on 1) the study-specific distributions of the added predictors (wider ranges leading to greater ) and 2) the relevance of the pooled for the added predictors to individual studies.
The D iscern predictor represents these fundamental characteristics of active sites by including features for the individual site whose catalycity is being predicted and also for its structural neighbors.
For every voxel the time course was regressed on a set of dummy-coded predictors representing the experimental conditions.
Design variables will be entered as predictors representing the treatment combinations, with the reference category representing minimal content, no non-smoker support and only passive diffusion.
As we did not have full scales, caution should be exercised in assuming that the 18-month predictors represent the full measures of temperament or psychopathology as they were originally intended.
For continuous variables, the hazard ratio represents a shift in the hazard that is due to a unit change in the predictor whereas for binary predictors, it represents the effect of the factor being present compared to its absence in calves.
In Equation (4), k represents mobile group of intellectual equipment port number, alpha predictor represents user requirements, and N represents user scale.
Prediction of ER status was included as a positive control for our gene expression-based predictors; it represents the upper threshold of how good a predictor can be (87% [95% CI: 0.74 0.94] −89%[95%% CI: 0.76 0.95] accuracy) on this dataset, providing a benchmark against which the chemotherapy response predictors can be judged (Supplementary Table 5).
PSORTb 3.0 is able to capture predictions for some of the archaeal-specific proteins and demonstrates superiority in performance compared to PSORTb 2.0 and to other Gram-positive bacterial SCL predictors, and now represents the first predictor specifically designed for the important domain of archaea.
