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The downward bias implies, furthermore, that Methods 1 to 3 are not suited for estimating heritability used to divide predictive ability to obtain predictive accuracy in genomic prediction for which genotypes are typically assumed to be correlated.
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In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation.
The three Methods 5, 6 and 7 were thus used to estimate predictive accuracy directly without first dividing predictive ability by the square root of heritability.
Typically, K-fold CV is often used to evaluate predictive models, in which the whole dataset is divided into K portions of approximately equal size.
This is because, despite estimating heritability, method 5 computes predictive accuracy directly, as do methods 6 and 7. Methods 5 to 7 were thus used to estimate predictive accuracy for the datasets with and without outliers directly without first dividing predictive ability by the square root of heritability.
The data set was randomly divided into two subsets consisting of an internal set (80%) and an external set (20%) in which the former set was used to constructing predictive models as full training and tenfold cross-validation (tenfold CV) while samples in the latter set was predicted using the aforementioned trained model.
These patterns were used to develop predictive tests, which were rapidly shepherded into clinical trials.
RBF was used to build predictive artificial neural network model.
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