(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut-off value of 0.541, a specificity of 0.746, and a sensitivity of 0.680. (B) Performance of the model in the validation set, which showed an AUC value of 0.745, an optimal cut-off value of 0.062, a specificity of 0.769, and a sensitivity of 0.659.</p
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
ROC curves of the PR (a) and CR (b) model for the best and worst training (solid) and the validation...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the sam...
<p>Shown here are the ROC curve Area-Under-Curve (AUC) scores, sensitivities and specificities for t...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
ROC curves of the PR (a) and CR (b) model for the best and worst training (solid) and the validation...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the sam...
<p>Shown here are the ROC curve Area-Under-Curve (AUC) scores, sensitivities and specificities for t...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...