Algorithms for RNA Folding: a Comparison of Dynamic Programming and Parallel Evolutionary Algorithms

Wiese, Kay C.; Hendriks, A.; Poonian, J.

doi:10.1109/cec.2005.1554721

Cited by 1 publication

(2 citation statements)

References 15 publications

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“…In this subsection, the results of HelixPSO are compared to the results of other algorithms. For RNA‐Predict we use the results that are presented by Wiese et al (2005), where average values have been presented, and by Wiese et al (2008), where the best values obtained for 30 random seeds and 12 different operator combinations are listed. Results for SARNA‐Predict average values were taken from Tsang and Wiese (2006) and best values are from Tsang and Wiese (2007a, b), where 100 runs were executed on each test sequence and different combinations of parameter values were used on each test sequence.…”

Section: Resultsmentioning

confidence: 99%

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Particle swarm optimization for finding RNA secondary structures

Geis

Middendorf

2011

International Journal of Intelligent Computing and Cybernetics

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This paper proposes a Particle Swarm Optimization (PSO) algorithm called HelixPSO for finding RNA secondary structures that have a low energy and are similar to the native structure. HelixPSO is compared to the recent algorithms RnaPredict, SARNA-Predict, SetPSO, and RNAfold. For a set of standard RNA test sequences it is shown that HelixPSO obtains a better average sensitivity than SARNA-Predict and SetPSO and is as good as RNA-Predict and RNAfold. When best values for different measures (e.g., number of correctly predicted base pairs, false positives, and sensitivity) over several runs are compared, HelixPSO performs better than RNAfold, similar to RNA-Predict, and is outperformed by SARNA-Predict. It is shown that HelixPSO complements Rna-Predict and SARNA-Predict well since the algorithms show often very different behavior on the same sequence. Furthermore, a parallel version of the HelixPSO is proposed and it is shown that good speedup values can be obtained for small to medium size PC clusters.

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Section: Resultsmentioning

confidence: 99%

“…Average sensitivity of G‐HelixPSO (PSO), RNA‐Predict (data from Wiese et al (2005)), SARNA‐Predict (SA) (data from Tsang and Wiese (2006)) and SetPSO (data from Neethling and Engelbrecht (2006))…”

Section: Table IIImentioning

confidence: 99%

Particle swarm optimization for finding RNA secondary structures

Geis

Middendorf

2011

International Journal of Intelligent Computing and Cybernetics

View full text Add to dashboard Cite

show abstract

Algorithms for RNA Folding: a Comparison of Dynamic Programming and Parallel Evolutionary Algorithms

Cited by 1 publication

References 15 publications

Particle swarm optimization for finding RNA secondary structures

Particle swarm optimization for finding RNA secondary structures

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