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Similar AUCs applied to the percentage dense area measurements.
Data shown are mean AUC vs. applied ASL volume.
Having studied the evolutionary patterns of miRNA genes, we next reexamined the data presented in [5] that showed higher accuracy for PITA in comparison to the other miRNA target prediction methods using the area under the curve (AUC) metric applied to 190 previously validated miRNA targets.
ROC curve and AUC were applied to evaluate the discriminatory power of each marker and combined CSF markers.
We produced receiver-operating characteristic (ROC) curve plots and calculated the area under the ROC curve (AUC) by applying the methods described by Chambless et al (2011).
Figure 3 presents the AUCs from applying PIPES to predict in vivo TF binding in the corresponding tissues for the same 11 TFs studied in the previous section.
To obtain a utility score per patient, the area-under-the curve method (AUC) will be applied [ 41].
For estimation of the significance of differences in AUC values we applied a paired-sample single-sided t-test.
A SCALE-UP reuse model derived from Figure 2 (rich-medium yeast data) resulted in 3 14% AUC increase when applied to the other yeast datasets (Supplementary Table S10).
The AUC was obtained applying the following procedure (see also Supplementary Material): we consider the set of all distances between nucleosome predictions and nucleosomes in the map <73 bp, we obtain a cumulative error curve.
The performance of the classifier was measured by the AUC-ROC during the optimization, and optimal parameters that resulted in the highest AUC-ROC were applied to test sets.
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