DEVELOPMENT OF AN EFFICIENT HYBRID METHOD FOR MOTIF DISCOVERY IN DNA SEQUENCES

Abstract

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.