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Figure 2 | BMC Bioinformatics

Figure 2

From: Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)

Figure 2

Summaries of performance using ROC curves. Results are only presented for BP because the MF results were too strongly affected by biases due to the E. coli annotations. A. Distribution of AUROCs for the GO terms evaluated. The mean performance across algorithms is shown in black. A simple aggregation algorithm does much better on average, shown in grey. B. Density plot showing the overlay of the ROC curves that make up the results shown for the aggregation algorithm in A, with areas of high density shown in lighter shades. Scattered light areas are artifacts due to the effects of GO groups with smaller numbers of genes. Note that the Prevalence method is guaranteed to generate AUROCs of 0.5 for all functions since it ranks all genes equally.

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