<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 for the independent test set.</p
<p>The area under the ROC (AUC) = 0.997 (95% confidence interval [CI] 0.991 to 1.000, <i>P</i><0.001...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>ROC curves for the IP4IC (training set) and P3 (validation set) using the BP-RS. The AUC for the ...
<p>Results are evaluated based on the benchmark dataset (A) and independent test dataset (B).</p
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the sam...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>The area under the ROC (AUC) = 0.997 (95% confidence interval [CI] 0.991 to 1.000, <i>P</i><0.001...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>ROC curves for the IP4IC (training set) and P3 (validation set) using the BP-RS. The AUC for the ...
<p>Results are evaluated based on the benchmark dataset (A) and independent test dataset (B).</p
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the sam...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>The area under the ROC (AUC) = 0.997 (95% confidence interval [CI] 0.991 to 1.000, <i>P</i><0.001...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
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