<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) with 95% confidence intervals (CI), for (a) entire cohort, unadjusted and for 100 repetitions of ten-fold stratified cross-validation (SCV), and (b) for each of the four time-frames after cross-validation against a model derived from data in the other three time-frames. The ten-fold SCV adjusted values of AUC and CI limits are the corresponding mean values among the 100 repetitions, and the solid black line is a LOESS smoothed curve for the 100 SCV adjusted ROC curves outlined in gray.</p
<p>Receiver operating characteristic curve (ROC) shows that the areas under ROC are approximately 0....
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>Areas under the curves (AUC) obtained in a 10-fold cross-validation setting. The AUC is averaged ...
<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p><b>(a)</b> The ensemble-based prediction model based on all five combined patterns has an area un...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
<p>Optimally, the curve should lie towards the upper left corner of the plot. Survival: assessed at ...
<p>Receiver operating characteristic curve (ROC) shows that the areas under ROC are approximately 0....
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>Areas under the curves (AUC) obtained in a 10-fold cross-validation setting. The AUC is averaged ...
<p>A. Predictive ability of the full and “conventional” models in the original population. ROC Curve...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p><b>(a)</b> The ensemble-based prediction model based on all five combined patterns has an area un...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
<p>Optimally, the curve should lie towards the upper left corner of the plot. Survival: assessed at ...
<p>Receiver operating characteristic curve (ROC) shows that the areas under ROC are approximately 0....
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>Areas under the curves (AUC) obtained in a 10-fold cross-validation setting. The AUC is averaged ...