<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+RNAz and Dynalign/SVM on the low-entropy range (<0.3) of the 4th testing set. (B) The high-specificity range of the ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+RNAz and Dynalign/SVM on the low-entropy range (<0.3) of the fourth testing set. (C) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+RNAz and Dynalign/SVM on the high-entropy range (>0.3) of the fourth testing set. (D) The high specificity range of the ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+RNAz and Dynalign/SVM on the high-entropy range (>...
<p>ROC curves from four analytic methods, (1) EZ-MAP, (2) “one vs. many” t-test, and (3) standard Z-...
<p>Note.–*The data in parentheses are 95% confidence intervals.</p>†<p>Sensitivity and specificity f...
<p>ROC curves of TPR versus FPR for optimal sets of biomarkers where averaged over SSVM models. T...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>The F1, F2, and F3 scores were computed for every point on the ROC curve from the training datase...
<p>ROC curves showing true-positive rates (sensitivity) plotted against the false-positive rate for ...
ROC curves are plotted using 10 randomly selected training and testing data sets using 80%, and 20% ...
<p>The ROC curves of the RF and SVM in four independent external validations for (a) Model I, (b) Mo...
<p>The ROC curves of the RF and SVM in internal five-fold cross validation for (a) Model I, (b) Mode...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>ROC curves from four analytic methods, (1) EZ-MAP, (2) “one vs. many” t-test, and (3) standard Z-...
<p>Note.–*The data in parentheses are 95% confidence intervals.</p>†<p>Sensitivity and specificity f...
<p>ROC curves of TPR versus FPR for optimal sets of biomarkers where averaged over SSVM models. T...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocARNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>(A) ROC curves for Multifind, Multifind trained without ensemble defect Z score, RNAz, LocaRNATE+...
<p>The F1, F2, and F3 scores were computed for every point on the ROC curve from the training datase...
<p>ROC curves showing true-positive rates (sensitivity) plotted against the false-positive rate for ...
ROC curves are plotted using 10 randomly selected training and testing data sets using 80%, and 20% ...
<p>The ROC curves of the RF and SVM in four independent external validations for (a) Model I, (b) Mo...
<p>The ROC curves of the RF and SVM in internal five-fold cross validation for (a) Model I, (b) Mode...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>ROC curves from four analytic methods, (1) EZ-MAP, (2) “one vs. many” t-test, and (3) standard Z-...
<p>Note.–*The data in parentheses are 95% confidence intervals.</p>†<p>Sensitivity and specificity f...
<p>ROC curves of TPR versus FPR for optimal sets of biomarkers where averaged over SSVM models. T...