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Fig. 5 | Genome Biology

Fig. 5

From: TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning

Fig. 5

Performance comparison of TargetRNA3 and existing tools for predicting targets of sRNA regulation. The performance of TargetRNA3 and five existing tools (CopraRNA, RNAup, IntaRNA, SRNARFTarget, and RNAplex) when predicting sRNA targets is shown. A ROC curves for the six tools are illustrated. The abscissa axis corresponds to the false-positive rate, i.e., 1.0 − specificity. The ordinate axis corresponds to the true-positive rate, i.e., the recall or sensitivity. Different thresholds for the values reported by a tool represent different points along the tool’s curve in the figure. The dotted line with unit slope indicates the performance of a naïve random tool. B A particular point along each curve in A, specifically the point at which each of the six curves intersects the vertical line corresponding to a false-positive rate of 0.05. B The sensitivity, i.e., recall or true-positive rate, is shown for the six tools when their specificity is 95%, i.e., their false-positive rate is 0.05. C The mean runtime in minutes per sRNA is shown for the six tools, with yellow error bars corresponding to the standard error

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