Permutation-based RNA secondary structure prediction via a genetic algorithm

Wiese, Kay C.; Deschenes, A.; Glen, Edward

doi:10.1109/cec.2003.1299595

Cited by 19 publications

(6 citation statements)

References 12 publications

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“…Mutation is done in RnaPredict by swapping two random indices in the permutation. Wiese et al ([43,41]) have investigated the optimization behavior of RnaPredict with a variety of crossover operators (such as edge recombination crossover (ERC) [39], OX2 [33], cycle crossover (CX) [25], and Partially Mapped Crossover (PMC) [11]) and different selection operators (Keep-Best-Reproduction (KBR), Standard Roulette Wheel Selection (STDS) [40]). Some versions of RnaPredict use 1-elitism.…”

Section: Rnapredictmentioning

confidence: 99%

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: Rnapredictmentioning

confidence: 99%

Particle swarm optimization for finding RNA secondary structures

Geis

Middendorf

2011

International Journal of Intelligent Computing and Cybernetics

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“…Initialize random population Evaluate fitness of individuals For all NUM GENERATIONS For all DEME COUNT For all DEME SIZE Reproduce by crossover in deme Mutate Employ replacement (STDS or KBR) Apply Elitism If MIGRATION INTERVAL, migrate Given the enourmous number of possible parameter combinations, the selection of parameter sets for these experi- ments was based on previously published experimental results [11,29]. The parameters specifically relating to the serial GA were chosen based on those which produced the best set of results in [29], and were set as follows: The global population was set to 700, with the crossover probability (P c ) set to 0.7.…”

Section: Methodsmentioning

confidence: 99%

“…The parameters specifically relating to the serial GA were chosen based on those which produced the best set of results in [29], and were set as follows: The global population was set to 700, with the crossover probability (P c ) set to 0.7. The mutation probability (P m ) varied as either 0.25 or 0.8.…”

Section: Methodsmentioning

confidence: 99%

The impact of pseudorandom number quality on P-RnaPredict , a parallel genetic algorithm for RNA secondary structure prediction

Wiese

Hendriks

Deschenes

et al. 2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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This paper presents a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures.

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“…The first (Table I). Given the enormous number of possible parameter combinations, the selection of parameter sets for these experiments was based on previously published experimental results [23]. Those which were fixed for both experiments are shown in Table II.…”

Section: A Runtime Test Resultsmentioning

confidence: 99%

“…Parameters which varied were based on those determined through prior research [23], [12], and were configured as follows:…”

Section: Comparison To Mfoldmentioning

confidence: 99%

A Detailed Analysis of Parallel Speedup in P-RnaPredict - An Evolutionary Algorithm for RNA Secondary Structure Prediction

Wiese

Hendriks

2006 IEEE International Conference on Evolutionary Computation

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The function of an RNA molecule is primarily established by its physical shape. As current physical structure determination methods are time consuming and expensive, there is great interest in finding computational structure prediction methods. P-RnaPredict is a parallel evolutionary algorithm for RNA secondary structure prediction. Two sets of experiments are performed on 5 known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA, and 16S rRNA). The first determines the actual speedup, and the second evaluates the performance of P-RnaPredict through comparison to mfold. PRnaPredict succeeds in predicting structures with higher true positive base pair counts and lower false positives than mfold on specific sequences.

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Permutation-based RNA secondary structure prediction via a genetic algorithm

Cited by 19 publications

References 12 publications

Particle swarm optimization for finding RNA secondary structures

Particle swarm optimization for finding RNA secondary structures

The impact of pseudorandom number quality on P-RnaPredict , a parallel genetic algorithm for RNA secondary structure prediction

A Detailed Analysis of Parallel Speedup in P-RnaPredict - An Evolutionary Algorithm for RNA Secondary Structure Prediction

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