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Open Access Highly Accessed Methodology article

Analysis of energy-based algorithms for RNA secondary structure prediction

Monir Hajiaghayi*, Anne Condon and Holger H Hoos

Author Affiliations

Computer Science Department, University of British Columbia, Vancouver, BC, Canada

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BMC Bioinformatics 2012, 13:22  doi:10.1186/1471-2105-13-22

Published: 1 February 2012

Additional files

Additional file 1:

95% bootstrap percentile confidence interval graphs for the F-measure average of the rsMEA and rsMFE.95% bootstrap percentile confidence intervals are shown for the F-measure average of the rsMEA (dashed red bars), and rsMFE (solid black bars) algorithms on the MA and S-Full sets and also different RNA classes in MA.

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Additional file 2:

Confidence interval width versus RNA class size in the MA set for the rsMEA and rsMFE methods. The figure shows the confidence interval width of RNA classes in the MA set for the rsMEA and rsMFE methods.

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Additional file 3:

Accuracy comparison of different prediction algorithms with various parameter sets on the S-Full-Test set. The table presents the prediction accuracy of different algorithms with different thermodynamic sets in terms of F-measure. The parameter set T99-MRF refers to the Turner99 parameters in MultiRNAFold format. BL* and CG* are the parameter sets obtained by the BL and CG approaches of Andronescu et al. [9], respectively. Also, the Turner99 parameter set is the parameter set obtained by Mathews et al. [3]. "n/a" indicates cases in which a given algorithm is not applicable to a parameter set, since that does not match the energy model underlying the algorithm. The highest accuracies for MEA and MFE are shown in bold.

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