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Their magnitudes are not much diminished, if at all, by the addition of other predictors (Models 2 and 3).
These in silico predictors (models) have been built (learned) using static conditional as well as time-series gene expression data.
Due to several interactions between sex and identified predictors, models were eventually constructed (and are reported) for men and women separately.
The inclusion of DCS and ERI scores as predictors (models 3 5) further increased the adjusted R. Results suggested that lower support, higher effort and lower rewards predicted higher anxiety.
Changes in each of the metabolic variables and/or fitness were entered into a separate regression model, alone (models B J) or in combination with other predictors (models K Q), in the presence of the dichotomous treatment group indicator, to evaluate their contributions.
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Model 1 includes only main effect predictors; model 2 adds the interaction term between percentage of female authors and percentage of international authors.
The remaining six predictors modeled the other phases of the trial (Fig. 1B).
To this end, we estimated RFX GLMs with azimuth predictors modeling the ILD.
Yet, when controlling for the shared variances, neither agreeableness nor conscientiousness were significant predictors (Model 5, Table 4).
We also tested for association adjusting for age, WAA, study site, and environmental predictors (Model 2, n = 557).
The two respective models employed are significantly better than models without any predictors (model including worm strain as factor: LRT, χ2 = 1285.63, d.f.
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