Receiver Operating Characteristic (ROC) curves for discriminating each tumor subtype and/or anatomical location, versus the rest within that cancer type, shown for breast cancer (A) and colorectal cancer (B). See S8 Fig for additional cancer types. TPR and FPR calculated in five-fold cross validation.</p
Determining the cancer type and molecular subtype has important clinical implications. The primary s...
<p>Comparison among the four different pathologic types of tumor by using ROC analysis.</p
<p>AUC, Area under the ROC Curve; All indicates the combination of E2F1, E2F2, E2F3, E2F4, E2F5, and...
(A) Receiver Operating Characteristic (ROC) curve for each cancer type versus the rest. Area under t...
(A) ROC curves for simulated whole-exome sequencing data, for one cancer type versus all others. Are...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
(A) Mean Area Under the Precision-Recall Curve (AUPRC) for each cancer type, shown for classifiers d...
<p>ROC curves at 5Mb (top panels) and 100Mb ctDNA CNV resolution (bottom panels) showing performance...
<p>When measuring accuracy of cell classification as cancerous or healthy, one should consider both ...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
<p>ROC analysis for comparisons of perfusion and diffusion parameters from the tumor core (A) which ...
<p>Performance validation using ROC curves. The AUC values of GroupRank and SingleRank achieved in e...
<p>For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) ar...
<p>Receiver Operator Characteristic (ROC) curves evaluated discriminatory properties of genes differ...
<p>The curves shown were obtained by processing quantified raw data by SigmaPlot 12.0 version softwa...
Determining the cancer type and molecular subtype has important clinical implications. The primary s...
<p>Comparison among the four different pathologic types of tumor by using ROC analysis.</p
<p>AUC, Area under the ROC Curve; All indicates the combination of E2F1, E2F2, E2F3, E2F4, E2F5, and...
(A) Receiver Operating Characteristic (ROC) curve for each cancer type versus the rest. Area under t...
(A) ROC curves for simulated whole-exome sequencing data, for one cancer type versus all others. Are...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
(A) Mean Area Under the Precision-Recall Curve (AUPRC) for each cancer type, shown for classifiers d...
<p>ROC curves at 5Mb (top panels) and 100Mb ctDNA CNV resolution (bottom panels) showing performance...
<p>When measuring accuracy of cell classification as cancerous or healthy, one should consider both ...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
<p>ROC analysis for comparisons of perfusion and diffusion parameters from the tumor core (A) which ...
<p>Performance validation using ROC curves. The AUC values of GroupRank and SingleRank achieved in e...
<p>For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) ar...
<p>Receiver Operator Characteristic (ROC) curves evaluated discriminatory properties of genes differ...
<p>The curves shown were obtained by processing quantified raw data by SigmaPlot 12.0 version softwa...
Determining the cancer type and molecular subtype has important clinical implications. The primary s...
<p>Comparison among the four different pathologic types of tumor by using ROC analysis.</p
<p>AUC, Area under the ROC Curve; All indicates the combination of E2F1, E2F2, E2F3, E2F4, E2F5, and...