The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets.
To validate the model, a classical two-phased hypothesis test has been performed and the validity of this test depends on the following important factors: (i) the independence between training and validation data; (ii) the volume and variety of the experiments.
Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping.
The Bayesian method used for association analyses in this study has been demonstrated to have higher accuracy even with different relationship scenarios between training and validation populations [31], [32].
Two sources of divergence between training and validation samples exist: clinical differences such as diversity in cancer subtype, drug response, or prognosis, and genomic differences, or differences between gene expression patterns observed in the training and validation samples.
Accuracy of individual GEBV is estimated using the genomic relatedness between training and validation animals.
* Distributions are significantly different between training and validation sets by two-tailed Student's t-test.
There were similar discordance rates and predictive values between training and validation groups.
Moreover, the relationships between training and validation animals have an impact on imputation accuracies [ 20].
As a result, QP may be less affected by different genetic backgrounds between training and validation sets than GWP.
Furthermore, the increase in relationships between training and validation populations was confounded with the increased size of the training population.
