Figure 2.

Comparison of mirSVR to other methods. (a) Spearman rank correlation (vertical bars) between prediction and observation for canonical seed targets as ranked by mirSVR score, context score, alignment score from miRanda and energy score from PITA. Rank correlations were computed between prediction scores and observed log expression changes for 17 test sets measuring mRNA expression changes following microRNA transfection in different cell lines and genetic backgrounds [21] (brown), five test sets measuring protein expression changes following microRNA transfection [17] (red), and three test sets measuring mRNA expression changes following microRNA inhibition [21,23,41] (orange). Ranking by mirSVR scores outperforms that by context scores in 21 out of the 25 test sets. (b) ROC curves (receiver operating characteristic) for mirSVR score versus context score for ranking the top 20% most downregulated targets (defined as true positives) and 20% of least downregulated targets (defined as true negatives) for the miR-192 transfection [21]. Shown here are the ROC curves up to 30% false positive detection. In this example, in the range shown, for a given false positive rate, mirSVR ranking yields an advantage of up to 10 percentage points in the rate of true positive prediction. (c) A summary of this ROC analysis over the 25 test sets, computing the area under the ROC curve (AUC) for mirSVR and context score and reporting the difference in performance (mirSVR AUC - context score AUC) for each test set. Overall, mirSVR score shows a statistically significant improvement over context score with a mean AUC of 0.80 as compared to 0.78 and outperforming context score in 19 (bars above the zero line) out of the 25 test sets (P-value < 0.006, signed rank test).

Betel et al. Genome Biology 2010 11:R90   doi:10.1186/gb-2010-11-8-r90
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