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Alternatively, using genomic profiles of known OGs and TSGs as a reference, machine learning based predictors can be trained to identify cancer genes [ 19].
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A simple screening instrument based on these predictors can be used at ICU discharge to determine the risk for new-onset physical disability.
However, previous studies have shown that human opinion, based on specific sets of predictors, can be quantified and predicted.
The selection of predictors can be either preselected by testing or chosen based on classification algorithm, and the final identified predictors are also considered as differential expressed.
Because the CDEN is based on the MLP, nonlinear relationships, including those involving complicated interactions between predictors, can be described by the modeling framework.
These predictors can be used to enhance CVCT promotions in Zambia and may be extended as a framework to other locales, with adaptation based on location-specific predictors of CVCT promotions.
However, the optimal selection of predictors can be problematic when focusing on a localized region.
The quantile regression model also allows considerable flexibility in selecting predictor variables in that different subsets of predictors can be used for different parts of the conditional distribution.
One-level dynamic branch predictors can be analyzed [56].
Predictors can be continuous or categorical variables.
LS-based prediction method, an adaptive predictor, can be improved by using penalized regression.
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