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We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets.
We propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes.
Clay minerals were then mapped using data mining model trees with a 10-fold cross validation to derive a mean prediction estimate and 90% prediction interval.
For each fixed λ2, the tuning parameter s is chosen by 10-fold cross validation to derive the smallest cross validation error.
Figure 5C summarizes our results, showing the correlation values using cross validation for regulatory networks derived from subcluster 1 (red), 2 (orange), 3 (green), and 4 (blue).
We applied a cross validation by first deriving the prediction model in the Erasmus MC-Sophia population and then cross validating the prediction model in the Haga-Juliana population and vice versa.
The confidence grouping derived from cross validation performance compared to random models performed well overall in identifying models that are outperformed by random in <5% of cases in EHR validation (Table 3).
Predictions by the five individual models derived from cross validation were combined using simple majority voting.
Cross validation performance was used to derive a confidence score for the predictions for each ADR generated on the full graph.
The performance of the algorithm was evaluated using leave one out cross validation and various performance indicators were derived.
The statistics employed to evaluate and compare the performance of the different models were derived from this cross validation.
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