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Within this context, this article presents GENIE3 (for "GEne Network Inference with Ensemble of trees"), a new GRN inference method based on variable selection with ensembles of regression trees.
Traditional models based on variable selection in a stepwise approach can lead to biased estimates [ 15].
The algorithm is based on variable selection using reduced-rank Partial Least Squares with a regularized elimination.
Therefore, GBLUP can generally be considered optimal with traits in compliance with the classical infinitesimal model, while models based on variable selection such as BayesB are considered preferable for traits with a genetic architecture including large or moderate effect quantitative trait loci (QTL) (Wimmer et al. 2013).
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The elimination of non-significant variables in regression analysis based on backward variable selection is similar to the pruning of the tree that contains the maximum number of terminal nodes.
Since there were many potential features for siRNA classification, random forests were used for feature selection based on variable importance scores.
Boulesteix and Strimmer [ 35] describe and refer the connection of PLS to gene selection based on "variable importance in projection" (VIP) indicator proposed by Musumarra et al. [ 36], which indicates the importance of genes in the used PLS latent components.
Multiple regression models were fit based on purposeful variable selection, the inclusion of variables with a P value of <0.10, maximization of R. Standard diagnostics were used to make the decision to use spatially weighted regression rather than spatial-lagged models and to assess the model fit (15).
The multivariate analysis resulted in a final multivariate panel of biomarkers selected from the initial candidate panel based on statistical variable selection performed within the Random Forests package in R (Breiman et al, 1984; Breiman, 2001).
The design of this last kernel is based on a variable selection step in order to obtain kernels defined on parsimonious sets of patterns.
Then, we propose the development of an algorithm for the prediction step based on Bayesian variable selection, Bayesian model averaging, sparse nonlinear regression, reformer geometry and theories of thermal radiation so that for each reforming tube, the prediction step can systematically identify predictors for the OTWT and simultaneously create a corresponding library of sub-prediction models.
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