<p>When measuring accuracy of cell classification as cancerous or healthy, one should consider both types of errors: false positives and false negatives (or more conventionally, true positives). This is illustrated by the Receiver Operating Characteristic (ROC) Curve. Lines indicate mean values, and error bars indicate bootstrapped 95% confidence intervals. Accuracy was measured using cross-validation; and chance value was determined using shuffle control.</p
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
<p>a. AUC = Area under the curve from Receiver Operator Curve (ROC); FP = False positive rate; PPV =...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
(A) Receiver Operating Characteristic (ROC) curve for each cancer type versus the rest. Area under t...
Receiver Operating Characteristic (ROC) curves for discriminating each tumor subtype and/or anatomic...
<p>A receiver-operating curve (ROC) was generated as a preliminary estimate of the accuracy of relat...
<p>*ROC plot for diagnostic accuracy presents true positive rate vs. false positive rate (or sensiti...
<p>Receiver Operator Characteristic (ROC) curves evaluated discriminatory properties of genes differ...
<p>True positive rate is denoted TPR and false positive rate is denoted FPR in the Figure. A. Evalua...
<p><b>A.</b> Summary of the false and true positive rates of the 29-gene panel in classifying CRC ca...
<p>The ROC curves were used to show the diagnostic ability of these selected differentially expresse...
<p>The area under the ROC curve (AUC) represents how accurate the individual and combined biomarkers...
(A) ROC curves for simulated whole-exome sequencing data, for one cancer type versus all others. Are...
<p>The area under the ROC curve (AUC) represents how accurate the individual and combined biomarkers...
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
<p>a. AUC = Area under the curve from Receiver Operator Curve (ROC); FP = False positive rate; PPV =...
<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect...
(A) Receiver Operating Characteristic (ROC) curve for each cancer type versus the rest. Area under t...
Receiver Operating Characteristic (ROC) curves for discriminating each tumor subtype and/or anatomic...
<p>A receiver-operating curve (ROC) was generated as a preliminary estimate of the accuracy of relat...
<p>*ROC plot for diagnostic accuracy presents true positive rate vs. false positive rate (or sensiti...
<p>Receiver Operator Characteristic (ROC) curves evaluated discriminatory properties of genes differ...
<p>True positive rate is denoted TPR and false positive rate is denoted FPR in the Figure. A. Evalua...
<p><b>A.</b> Summary of the false and true positive rates of the 29-gene panel in classifying CRC ca...
<p>The ROC curves were used to show the diagnostic ability of these selected differentially expresse...
<p>The area under the ROC curve (AUC) represents how accurate the individual and combined biomarkers...
(A) ROC curves for simulated whole-exome sequencing data, for one cancer type versus all others. Are...
<p>The area under the ROC curve (AUC) represents how accurate the individual and combined biomarkers...
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
Receiver operating characteristic (ROC) curves are a powerful and fl exible tool to identify differe...
<p>a. AUC = Area under the curve from Receiver Operator Curve (ROC); FP = False positive rate; PPV =...