<p>(A) A bar plot describing the predicted AUC obtained over the combined datasets of the same cancer type using a five-fold cross validation procedure for MGE-SVM (red bars) and MCF (blue bars) classifiers. AUC denotes the area under the curve. Error bars represent one standard deviation, and p-values are for a one-sided, paired-sample t-test for the AUC of each of the five folds. (B), (C) present the receiver operating characteristic (ROC) curves obtained in the classification of the lung and breast cancer combined datasets, respectively.</p
<p>(A,B) ROC analysis of lung cancer patients and healthy controls, and healthy controls as a negati...
*<p>Model A: Five features selected by backwards elimination: (FeretY_ave, MaxDiameter_ave, Elongati...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
<p>Performance validation using ROC curves. The AUC values of GroupRank and SingleRank achieved in e...
(A) Bar graphs showing the number of differentially expressed CSR transcripts between each pairwise ...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
<p>For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) ar...
Receiver Operating Characteristic (ROC) curves for discriminating each tumor subtype and/or anatomic...
ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...
<p>Red, blue, and green curve denotes 5-fold cross-validation prediction performance of Bi-profile B...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
<p>ROC curves at 5Mb (top panels) and 100Mb ctDNA CNV resolution (bottom panels) showing performance...
No single biomarker for cancer is considered adequately sensitive and specific for cancer screening....
<p>The bar plots correspond to the average area under the ROC curve obtained from five widely used s...
<p>(A,B) ROC analysis of lung cancer patients and healthy controls, and healthy controls as a negati...
*<p>Model A: Five features selected by backwards elimination: (FeretY_ave, MaxDiameter_ave, Elongati...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...
<p>Performance validation using ROC curves. The AUC values of GroupRank and SingleRank achieved in e...
(A) Bar graphs showing the number of differentially expressed CSR transcripts between each pairwise ...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
<p>For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) ar...
Receiver Operating Characteristic (ROC) curves for discriminating each tumor subtype and/or anatomic...
ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...
<p>Red, blue, and green curve denotes 5-fold cross-validation prediction performance of Bi-profile B...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
<p>The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-E...
<p>ROC curves at 5Mb (top panels) and 100Mb ctDNA CNV resolution (bottom panels) showing performance...
No single biomarker for cancer is considered adequately sensitive and specific for cancer screening....
<p>The bar plots correspond to the average area under the ROC curve obtained from five widely used s...
<p>(A,B) ROC analysis of lung cancer patients and healthy controls, and healthy controls as a negati...
*<p>Model A: Five features selected by backwards elimination: (FeretY_ave, MaxDiameter_ave, Elongati...
<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing ...