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Development of an Efficient Hybrid Method for Motif Discovery in DNA Sequences | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 6، دوره 44، شماره 1، تیر 2012، صفحه 63-75 اصل مقاله (409.29 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2012.42 | ||
| نویسندگان | ||
| Reza Akbari* 1؛ Vahid Zeighami2؛ Koorush Ziarati3؛ Ismail Akbari4 | ||
| 1Corresponding Author, Reza Akbari is with the Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran, (email: akbari@sutech.ac.ir) | ||
| 2Vahid Zeighami is with the Department of Mathematics and Industrial Engineering, Ecole Polytechnique, de Montreal, Montreal, Quebec, Canada, (email: vahid.zeighami@polymtl.ca) | ||
| 3Koorush Ziarati is with the Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran, (email: ziarati@shirazu.ac.ir) | ||
| 4Ismail Akbari is a graduated student from Department of Industrial Engineering, Iran University of Science of Science and Technology, Tehran, Iran, (email: ismail.akbari80@gmail.com) | ||
| چکیده | ||
| This work presents a hybrid method for motif discovery in DNA sequences. The proposed method called SPSO-Lk, borrows the concept of Chebyshev polynomials and uses the stochastic local search to improve the performance of the basic PSO algorithm as a motif finder. The Chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed by the basic PSO algorithm. Under this method, to balance between exploration and exploitation, at each iteration step, a local region is associated with each candidate particle, and a local exploration performed in this blob. The stochastic local search employs an intelligent repulsion/attraction mechanism to navigate a particle to explore this local region beyond that defined by the search algorithm to achieve a better solution. Over the successive iterations, the size of local region dynamically decreases. Also a non-linear dynamic inertia weight is introduced to further improve the performance of SPSO-Lk approach. The SPSO-Lk is tested on different sets of simulated and real nucleotide sequences to discover implanted DNA motifs. Experimental results show that the SPSO-Lk is effective, and provides competitive results in comparison with the performance of other algorithms investigated in this consideration. | ||
| کلیدواژه‌ها | ||
| Particle Swarm Optimization؛ Stochastic Local Search؛ Motif Discovery | ||
| عنوان مقاله [English] | ||
| Development of an Efficient Hybrid Method for Motif Discovery in DNA Sequences | ||
| چکیده [English] | ||
| This work presents a hybrid method for motif discovery in DNA sequences. The proposed method called SPSO-Lk, borrows the concept of Chebyshev polynomials and uses the stochastic local search to improve the performance of the basic PSO algorithm as a motif finder. The Chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed by the basic PSO algorithm. Under this method, to balance between exploration and exploitation, at each iteration step, a local region is associated with each candidate particle, and a local exploration performed in this blob. The stochastic local search employs an intelligent repulsion/attraction mechanism to navigate a particle to explore this local region beyond that defined by the search algorithm to achieve a better solution. Over the successive iterations, the size of local region dynamically decreases. Also a non-linear dynamic inertia weight is introduced to further improve the performance of SPSO-Lk approach. The SPSO-Lk is tested on different sets of simulated and real nucleotide sequences to discover implanted DNA motifs. Experimental results show that the SPSO-Lk is effective, and provides competitive results in comparison with the performance of other algorithms investigated in this consideration. | ||
| کلیدواژه‌ها [English] | ||
| Particle Swarm Optimization, Stochastic Local Search, Motif Discovery | ||
| مراجع | ||
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