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Next we evaluate the performances of the built SGB model in the testing set.
(5) Side effect probabilities (the output of the model) were calculated for the drugs in the test set using the coefficients learned on the training set.
Tables in the test set do not appear in the training set, so a system must be able to generalize to unseen tables.
Finally, a support vector machine (SVM) compares feature vectors in the testing set with those ones in the training set, implements multi-class classification and estimates unknown applied forces in the testing set.
In each cycle, the samples in the testing set are included into the current training set.
We then calculated the area under receiver operating characteristic (ROC) curve of survival in the testing set.
As indicated in the table, SVM correctly classifies almost all instances in the testing set.
Similar results were obtained in the testing set as well (Figure 3A).
We evaluated a 35-gene signature reported in the original publication in the test set including 35 patients (Table 1).
Genes with concordant fold change in the test and training set and greater than 1.8 fold differences in the test set were determined to be biologically validated.
It may reflect a similarity in the test set structures.
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