Abstract
Multiple alignments of biological nucleic acid sequences are one of the most commonly used techniques in sequence analysis. These techniques demand a big computational load. We present a Genetic Algorithms (GA) that optimizes an objective function that is a measure of alignment quality (distance). Each individual in the population represents (in an efficient way) some underlying operations on the sequences and they evolve, by means of natural selection, to better populations where they obtain better alignment of the sequences. The improvement of the effectiveness is obtained by an elitism operator specially designed and by initial bias given to the population by the background knowledge of the user. Our GA presents some characteristics as robustness, convergence to solution, extraordinary capability of generalization and a easiness of being coded for parallel processing architectures, that make our GA very suitable for multiple molecular biology sequences analysis.
This research has been supported by the Spanish National Research Institution ”Comisión Interministerial de Ciencia y Tecnología-CICYT”, Project TIC2002- 03519.
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References
Huang, X.:On Global Sequence Alignment. Computer Applications in the Biosciences 10: 227–235, 1994.
Thompxon, J. D., Higgins, D. and Gibson, T.: Clustal W. Nucleic Acids Research. 22: 4673–4680, 1995.
Eddy, S.:Multiple Alignment using Hidden Markov Models. Proc. Intelligent Systems for Molecular Biology. AAAI Press.:114–120, 1995.
Morató, C., Seijas, J.: ”Genetic algorithms for DNA/RNA sequences comparison”. European Simulation Symposium (ESS’96). The Society for Computer Simulation. Génova. Italia, 1996.
Rocha, R., Morató, C. and Seijas, J.: ”Multiple protein sequences comparison by genetic algorithm”. Applications and Science of Computational Intelligence, S.K. Rogers, D.B. Fogel, J. C. Bezdek, B. Bossacchi, Editors,Proceedings of SPIE 3390,:99–102, 1998.
Seijas, J., Morató, C. and Andina, D.: ”Biological Sequences Analyzed by Means of Genetic Algorithms: An efficient way for Alignment ”. WSEAS,ISPRA2002,2002.
Goldberg, D. E.: Genetics Algorithms in Search, Optimitation andMachine Learning. Addison-Wesley Publishing Company, Inc,1989.
Gary W.G. and Thomas C. W.: An Enhanced Genetic Algorithm for Solving the High-Level Synthesis Problems of Scheduling, Allocation, and Binding. International Journal of Computational Intelligence and Applications. 1,(19):91–110, 2001.
Seijas, J., Morató, C. and Sanz G. J. L.: ”Genetic algorithms: Two Different Elitism Operators for Stochastic and Deterministic Applications”. Parallel Processing and Applied Mathematics, R. Wyrzykowski, J. Dongarra, M. Paprzycki, J. Wasniewski, Editors, 4th International Conference, PPAM 2001. LNCS 2328: 617–625, 2001.
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Seijas, J., Morató, C., Andina, D., Vega-Corona, A. (2003). Improving the Efficiency of Multiple Sequence Alignment by Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_46
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DOI: https://doi.org/10.1007/3-540-44869-1_46
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