We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across folds, the AUC averaged 0.804 (s.t.d. = 0.031).</p
The x-axis shows the false positive rate and the y-axis denotes the true positive rate. These rates ...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
<p>As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7),...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
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 ROC curve for predicting TPS score >5 in fig 3 (left), the AUC for TPS score >5 was 0.73 (95%...
The x-axis shows the false positive rate and the y-axis denotes the true positive rate. These rates ...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
<p>As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7),...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
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 ROC curve for predicting TPS score >5 in fig 3 (left), the AUC for TPS score >5 was 0.73 (95%...
The x-axis shows the false positive rate and the y-axis denotes the true positive rate. These rates ...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
<p>As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...
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