Evolving stochastic context-free grammars for RNA secondary structure prediction

BMC Bioinformatics

Table 2 Sensitivity and specificity of evolved SCFGs using different training and testing methods

	Grammar	KH99^′	GG1	GG2	GG3	GG4	GG5	GG6	Best
	Grammar found by		Local	IO	IO	CYK	CYK	CYK
CYK	Sensitivity	0.496	0.505	0.330	0.374	0.474	0.469	0.526	0.675
	PPV	0.479	0.481	0.258	0.322	0.454	0.467	0.479	0.585
	F–score	0.478	0.441	0.426	0.435	0.461	0.339	0.461	0.622
IO	Sensitivity	0.387	0.392	0.408	0.413	0.373	0.404	0.410	0.450
	PPV	0.552	0.517	0.551	0.550	0.566	0.556	0.583	0.584
	F–score	0.461	0.443	0.473	0.470	0.449	0.471	0.488	0.493

The sensitivities, PPVs, and F–scores of grammars GG1–GG6 and KH99^′ on the evaluation set, using different methods of training and testing. 'CYK’ indicates that the CYK algorithm was used, and 'IO’ that the inside and outside algorithms were used. The column 'Best’ was calculated by selecting, for each structure, the prediction with the highest F–score, and then recording the sensitivity, PPV, and F–score for that prediction. It is perhaps not surprising that the 'best’ predictions for CYK are better than the 'best’ predictions for IO, as IO is in some sense averaging over all predictions. One might expect the predictions to be more similar than those from CYK, as seen by comparing IO values for GG6 and 'best’, giving less increase when considering those with best F–score.

ISSN: 1471-2105