Algorithms for Molecular Biology

Table 1 Prediction accuracy of competitive RNA-RNA joint secondary structure prediction methods.

From: Fast prediction of RNA-RNA interaction

	Sensitivity				PPV				F-measure
RNA-RNA interaction pairs	inRNAs	EBM	SPM	LM	inRNAs	EBM	SPM	LM	inRNAs	EBM	SPM	LM
CopA-CopT	1.000	0.909	0.955	0.864	0.846	0.800	0.778	0.760	0.917	0.851	0.857	0.809
DIS-DIS	1.000	0.786	0.786	0.786	1.000	0.786	0.786	0.786	1.000	0.786	0.786	0.786
IncRNA₅₄-RepZ	0.875	0.917	0.875	0.875	0.792	0.830	0.778	0.778	0.831	0.871	0.824	0.824
R1inv-R2inv	0.900	0.900	1.000	1.000	0.900	0.947	1.000	1.000	0.900	0.923	1.000	1.000
Tar-Tar*	1.000	1.000	1.000	1.000	0.875	0.933	0.875	0.875	0.933	0.965	0.933	0.933
Average	0.955	0.902	0.923	0.905	0.883	0.859	0.843	0.840	0.916	0.879	0.880	0.870

This Table shows the sensitivity, PPV and F-measure for RNA-RNA joint secondary structure prediction by (1) inRNAs, (2) the grammatical approach by Kato et al. [13] (denoted by EBM as energy-based model), and (3) the DP methods for two models presented by Alkan et al. [1] (denoted by SPM as stacked-pair model and LM as loop model). The dataset is compiled by Kato et al. [13].

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