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Genetic Algorithm based Optimizer for RNA Secondary Structure Prediction

by Gyan Prakash Sagar, Shailendra Singh, Padmavati
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 6
Year of Publication: 2011
Authors: Gyan Prakash Sagar, Shailendra Singh, Padmavati
10.5120/2959-3937

Gyan Prakash Sagar, Shailendra Singh, Padmavati . Genetic Algorithm based Optimizer for RNA Secondary Structure Prediction. International Journal of Computer Applications. 24, 6 ( June 2011), 24-28. DOI=10.5120/2959-3937

@article{ 10.5120/2959-3937,
author = { Gyan Prakash Sagar, Shailendra Singh, Padmavati },
title = { Genetic Algorithm based Optimizer for RNA Secondary Structure Prediction },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 6 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number6/2959-3937/ },
doi = { 10.5120/2959-3937 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:34.818037+05:30
%A Gyan Prakash Sagar
%A Shailendra Singh
%A Padmavati
%T Genetic Algorithm based Optimizer for RNA Secondary Structure Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 6
%P 24-28
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper represents the optimization of RNA secondary structure prediction in RNA molecule by using the genetic algorithm based on binary crossover operators. Using the selection function STDS keep best reproduction for RNA prediction. Take the number of individual RNA sequence sets from the Rfam family database and calculate the lowest free-energy in the individual RNA sequence sets. Apply the fitness function for optimize the lowest free-energy in the each individual sequence sets. Which one individual sequence set have the lowest free-energy that individual sequence set will be predict the best optimizer secondary structure in the RNA individual sequence and used the RNA fold algorithm for calculating the free-energy in the RNA molecules.

References
  1. E. W. Steeg, Artificial Inteligence and Molecular Biology, chapter Neural Networks, Adaptive Optimization, and RNA Secondary Structure Prediction, pp. 121-60, American Association for. Artificial Intelligency, Menlo Park, CA, USA, 1993.for. Artificial Intelligency, Menlo Park, CA, USA,1993.
  2. Bohar, H., Bhor, J., Brunak, S., Cotterill, R.M.J., Fredholm, H., Lautrup, B. and Peterson, S.B. (1990) Febs Letters, 261,43-46.
  3. O’Neill, M.C. (1992) Nucleic Acids Res., 20, 3437-3477.
  4. Q. Liu, X. Ye, and Y. Zhang, “A Hopfield neural network based algorithm for rna secondary structure prediction,” Proc. of the First International Multi-Syposiums on Computer and Computational Sciences (IMSCCS’06), pp. 1-7, 2006
  5. Yuan Xi-min, Li Hong-yan, Li Shu-kun, Cui Guang-tao. The application of Neuran Networks and Genetic Algorithm in water science
  6. m, Bejing. China Water Conservancy and Hydropower Press.2002,8.
  7. K. C. Wiese, E. Glen. “A permutation Based Genetic Algorithm for the RNA Folding Problem: A Critical Look at Selection Strategies, Crossover Operators and Representation Issues”, BioSysrem-Special Issue on Computational intelligence in Bioinformatics, Fogel G, Corne d, (eds.) in press, 2003.
  8. Mathews, D.H., Sabina, J., Zuker, M., Turner, D.H.: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Bio., 288:911-11940, 1999.
  9. F. H. D. van Batenburg. A. P. Gultyaev, and C. W. A. Pleij, “An APL-programmed genetic algorithm for the prediction of RNA secondary structure,” Jouranal of Theoretical Biplogy, vol. 174,pp. 26-280, 1995.
  10. M. Zukar. Mfold Web Server for Nucleic Acid Folding and Hybridization Prediction. Nuc. Acid. Res., 31:3406, 2003.
  11. B. A. Shapiro and J. Navetta. A Massively Parallel Genetic Algorithm for RNA Secondary Structure Prediction. J. supercomput, 8:195-207, 1994.
  12. T. Starkweather, S. McDanial, C. Whitely, K. Matheas, and D. Whitely, “A comparison of genetic sequencing operators,” in Proceeding of the Fourth International Conference on Genetic Algorithms, R. Belew and L. Booker, Eds, Los Altos: Morgan Kaufmann Publishers, 1991, pp. 69-76.
  13. K. C. Wiese and S. D. Goodwin, “Keep-Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in.” Constraints, vol. pp. 399-422, 2001.
  14. J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In J. J. Grefenstette, editior, Proceeding of the Second International Conference on Genetic Algorithms and their Application, pages 14-21, Hillsdale, New Jersey, USA, 1987. Lawrence Erlbaum Associates.
  15. Kay Wiese and Scott D. Goodwin. Convergence characteristics of keep-best reproduction. In SAC ’99. Proceeding of the 1999 ACM Symposium on Applied Computing 1999, pages 312-318. ACM, 1999.
  16. Kay Wiese and Scott D. Goodwin. Keep-best reproduction: A local family competition selection strategy and the environment it flourishes in. Constraints, 6(4):399-422, 2001
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm RNA secondary structure RNA folding minimum free-energy Genetic algorithm representation Genetic algorithm crossover operators

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