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Here we built a multivariate regression model to infer activity changes of TFs beyond copy number and methylation status variations using TF binding sites as features (see Methods).
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build a multivariate regression model including all variables with p < 0.05. .
build a univariate regression for each independent variable, build a multivariate regression model including all variables with p < 0.05.
Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA.
Factors with meaningful correlations (P≤0·1) were used to build a multivariate regression analysis model.
Next we built a multivariate logistic regression model using manual forward selection.
Finally, we built a multivariate linear regression model with all predictor variables to disentangle the factors explaining geographical variation in absolute and relative pandemic death rates.
Finally, we built a multivariate linear regression model with all predictor variables to disentangle the factors explaining geographical variation in pandemic onset and peak timing across Chilean regions.
We then built a multivariate logistic regression model to identify risk factors for HEV disease; we used backward stepwise selection and defined the best model as the one with the lowest Akaike information criterion [ 35].
We built a multivariate Cox-regression model to analyze the prognostic impact of SBP.
In addition, we will build a multivariate multinomial regression model of best fit to examine the variability in patient and public views about EHRs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com