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On average, when validating on the outer layer, CT has the highest SI (0.83) with %VE of 26.31% ± 38.26% and after applying EQ.PET filter, AT40 has the highest SI (0.83) with %VE of 27.98% ± 59.58%.
Cross-validation on the final model was performed by partitioning the dataset into four complementary subsets, training on the 75% of the data (training set) and validating on the other 25% (prediction set).
Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach.
In this method, the data set was randomly divided into 10 equal sub data sets, where model fitting occurred for each selection of nine-tenths of the data while validating on the final tenth.
The results (Table 3) indicate that the Cox-model filter performs comparably with these two algorithms when either testing on microarray data itself or validating on the independent RNA-seq data.> -wrap-foot> Note: In this comparison, we used 1,889 genes with extra barcode-frequency filtering and their actual expression values.
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The minimized signatures were built on the training dataset and be validated on the validation dataset.
The model with the best performance was validated on the validation set.
The local recurrence profile and its associated threshold value were subsequently validated on the validation set.
The multiple imputation strategy developed was then validated on the 30% validation sample.
Correspondingly, the swap models are signatures built on validation dataset and be validated on the training dataset.
Therefore, we applied this analysis on one subset (the training set), and validated on the other independent subset (the testing set).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com