<p>The true positive rate (TPR) is plotted as a function of false positive rate (FPR) for the 7 miRNAs individually (upper panel) and for selected combination of them (lower panel). We used the linear discriminant analysis (LDA) to build a model on the training data (11 AD and 17 controls) to predict the AD status of 37 patients (20 AD and 17 controls). These miRNA combinations are all characterized with high area under the curve (AUC) values (>0.93).</p
<p>Receiver operating characteristic (ROC) curve analysis of expression ratio of (a) PBMC miR-150/RN...
<p>Receiving Operator Characteristic (ROC) curves for individual miRNAs and combination of miR-19b a...
<p>Receiver operating characteristic (ROC) curves to assess the utility of miRNAs to differentiate L...
<p>ROC curves regarding diagnostic power to distinguish UA patients from non-UA cases for 7 selected...
<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cr...
<p>ROC analysis allows the evaluation of the binary classification variable (MSAP = 0, SAP = 1) at...
<p>The red dot indicates the selected score cutoff of −8.12, which achieves the highest true positiv...
<p>Receiver operator characteristic curve analysis of individual miRNAs (A: miR423-5p; B: miR30d; C:...
<p>The ROC curves were used to show the diagnostic ability of miRNA signature and miRNA signature wi...
<p>Serum (A) miR-744, (B) miR-376c, (C) miR-221 and (D) let-7e yielded the largest areas under the R...
<p>Receiver operating characteristics (ROC) curves for the detected serum MIRNAs. MiR-1281 and miR-1...
<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the fo...
<p>The miRNA expression signature of miR-449a/200a/200b discriminated endometriotic lesions (n = 22)...
<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the fo...
<p>ROC curves generated using the prognosis information and expression levels of the 5-miRNA signatu...
<p>Receiver operating characteristic (ROC) curve analysis of expression ratio of (a) PBMC miR-150/RN...
<p>Receiving Operator Characteristic (ROC) curves for individual miRNAs and combination of miR-19b a...
<p>Receiver operating characteristic (ROC) curves to assess the utility of miRNAs to differentiate L...
<p>ROC curves regarding diagnostic power to distinguish UA patients from non-UA cases for 7 selected...
<p>(A)ROC curve displaying the performance of different ML-based miRNA predictors in the ten-fold cr...
<p>ROC analysis allows the evaluation of the binary classification variable (MSAP = 0, SAP = 1) at...
<p>The red dot indicates the selected score cutoff of −8.12, which achieves the highest true positiv...
<p>Receiver operator characteristic curve analysis of individual miRNAs (A: miR423-5p; B: miR30d; C:...
<p>The ROC curves were used to show the diagnostic ability of miRNA signature and miRNA signature wi...
<p>Serum (A) miR-744, (B) miR-376c, (C) miR-221 and (D) let-7e yielded the largest areas under the R...
<p>Receiver operating characteristics (ROC) curves for the detected serum MIRNAs. MiR-1281 and miR-1...
<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the fo...
<p>The miRNA expression signature of miR-449a/200a/200b discriminated endometriotic lesions (n = 22)...
<p>AUC: area under the curve. The AIC model identified as the best performing miRNA signature the fo...
<p>ROC curves generated using the prognosis information and expression levels of the 5-miRNA signatu...
<p>Receiver operating characteristic (ROC) curve analysis of expression ratio of (a) PBMC miR-150/RN...
<p>Receiving Operator Characteristic (ROC) curves for individual miRNAs and combination of miR-19b a...
<p>Receiver operating characteristic (ROC) curves to assess the utility of miRNAs to differentiate L...