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The precision of both the ATR and RID methods was investigated using the prediction set samples.
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One expects that if gene set A is always differentially expressed when gene set B is on, the state of gene set A can be predicted using the prediction of gene set B, even if a direct prediction of A is not possible from the rat data.
Since the genome sequences contain the full information of a species and the database of non-coding RNA of pigs is quite incompletely, thus we used whole genome sequences to construct the prediction set (PR-S).
Both plots were generated using the predictions on the test set calculated with the Linear Stacking ensembles (Tables 1, 3).
The calibration set was used to build a calibration model, while the prediction set was used to assess the predictive performance and accuracy of the final model.
Representative calibration set was selected according to spectra diversity using (Kennard and Stone 1969) algorithm for developing calibration models between the spectral data and laboratory reference values, while the prediction set was used for validation.
A prediction model will be built using the training set and evaluated using the validation set (forward prediction) and vice versa (backward prediction).
It can be observed that k and R prediction using the input set and ANFIS procedure is acceptable and satisfactory (Fig. 5).
The models were validated and tested through the use of the external prediction set of compounds, leave-one-out and leave-many-out cross validation methods, applicability domain analysis and Y-randomization.
Cross-validation is a statistical technique of splitting data into training and validation sets by using the validation set to evaluate prediction ability of the trained model.
Using a seven-gene prediction set, the estimated prediction error was 3% (1/34).
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