Authors: Yi Zhang; Meng Zhang; Zhili Pei
Addresses: Department of Computer Science, Jilin Business and Technology College, Changchun 130062, China; Military Simulation Technology Institute, Air Force Aviation University, Changchun 130022, China. ' College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China. ' College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao 028000, China; College of Mathematics, Jilin University, Changchun 130012, China
Abstract: In this paper, a hybrid optimisation algorithm for the motif detection problem of biological sequences is presented. Our method is improved Gibbs sampling method by employing an improved ant colony optimisation (ACO) algorithm. The goal of our method is to reduce the required computing time and get better solution. First, we find a set of better candidate positions for revising the motif by using an improved ACO. Then we use these candidate positions as the input to the Gibbs sampling method. The simulation results show that by employing our improved algorithm, both efficiency and quality for detecting motifs are improved compared with simple Gibbs sampling method.
Keywords: ant colony optimisation; ACO; Gibbs sampling algorithm; motif detection; hybrid optimisation; biological sequences; bioinformatics; motifs.
International Journal of Computer Applications in Technology, 2012 Vol.44 No.2, pp.88 - 93
Published online: 23 Aug 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article