Selcuk Aslan

A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences.

The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

A new artificial bee colony algorithm to solve the multiple sequence alignment problem

Aligning three or more sequences simultaneously is one of the most challenging problems in bioinformatics. In this paper, a new Artificial Bee Colony algorithm ABC-Aligner is proposed to solve multiple sequence alignment. Multiple alignments obtained from ABC-Aligner are compared in terms of the SPS, COFFEE and standard SP scores with Particle Swarm Optimisation PSO, Genetic Algorithm GA and basic Artificial Bee Colony ABC algorithm; with Sequence Alignment by Genetic Algorithm SAGA and CLUSTALX software packages; and with nine well-known alignment tools including CLUSTALW, CLUSTAL OMEGA, DIALIGN-TX, MAFFT, MUSCLE, POA, Probalign, Probcons and T-COFFEE, over the sequences extracted from the BAliBASE 1.0, 3D_ali and BAliBASE 3.0 benchmark datasets, respectively. From the simulation results, it is concluded that proposed ABC-Aligner algorithm outperforms the other population-based meta-heuristics and obtains very close or better scores than software packages used in the experiments without requiring any a priori information or applying complex procedures.

Alignment of biological sequences by discrete Artificial Bee Colony algorithm

Multiple Sequence Alignment (MSA) problem based on aligning three or more biological sequences by considering the maximization of similarity is one of the most important problem in bioinformatics. In this paper, a new Artificial Bee Colony (ABC) algorithm inspired by the intelligent foraging behavior of honey bees is proposed to generate optimum solutions. Multiple alignments obtained by the proposed ABC algorithm are compared with the basic ABC algorithm, Genetic algorithm and Particle Swarm Optimization algorithm in the literature over the Relative Sum of Pairs (SPS) score. From the simulation results, it is concluded that the modifications made on the employed and onlooker bee phases have increased the quality of the alignments.

Discovery of conserved regions in DNA sequences by Artificial Bee Colony (ABC) algorithm based methods

The conserved regions between genomic sequences extracted from the different species give important information about the complex regulatory mechanisms of the transcription and translation processes. However, identification of these regulatory regions that are short DNA segments and called motifs is a major challenge in bioinformatics. With the avalanche of the newly sequenced genomic data and our evolving understanding of the characteristics of the regulatory mechanisms, there is still a need for developing fast and accurate motif discovery techniques or considerably refinement on the existing models. In this paper, we presented two different Artificial Bee Colony algorithm based motif discovery techniques and investigated their serial and parallelized implementations. Experimental studies on the three real data sets showed that the proposed methods outperformed other metaheuristics in terms of similarity values of the predicted motifs.

Performance analysis of artificial bee colony algorithm on ARM based mobile platform

Improved mobile devices have the computational power and software support for processing complex algorithmic expressions with the similar execution time when compared to the conventional processors. In this study, we investigated both serial and parallel implementations of Artificial Bee Colony (ABC) algorithm that is one of the most important swarm intelligence based algorithms by solving different types of numerical problems on a mobile platform. Experimental studies showed that serial and parallel implementations of ABC algorithm are suitable for execution on mobile processors and there is a correspondence between the final solutions obtained by ABC algorithm on a mobile processor and the final solutions obtained by ABC algorithm on a conventional processor.

Accelerating search tree based brute force motif discovery technique on a processor cluster

Determination of conserved regions that plays vital roles on regulation of transcription and translation processes is one of the most challenging problems in bioinformatics. However, with the increasing power of distributed computing systems, solving these types of combinatorial problems by utilizing parallelized brute force or exhaustive search algorithms recently has gained popularity. In this paper, we investigated the parallelized implementation of a search tree based brute force technique to find motifs with different lengths. Experimental studies showed that parallelization of the brute force techniques with less communication overhead is significantly increased the usability of them to analyze long nucleotide sequences.

A new emigrant creation strategy based on local best sources for parallel Artificial Bee Colony algorithm

Artificial Bee Colony algorithm, inspired by the foraging behavior of real honey bees, is one of the most important swarm intelligence based optimization algorithms. Like other population based evolutionary computation techniques, Artificial Bee Colony algorithm is suitable for parallelization on distributed architectures. In this paper, we presented a new emigrant creation strategy that is being distributed between subcolonies running simultaneously on the independent compute nodes. The running times and objective function values obtained by the parallelized Artificial Bee Colony algorithm with the proposed model on different number of compute nodes are compared with the sequential counterpart of the algorithm and it is seen that convergence performance of the parallelized Artificial Bee Colony algorithm is significantly improved with the proposed emigrant creation strategy.

Accelerating Discrete Haar Wavelet Transform on GPU cluster

The Discrete Haar Wavelet Transform has a wide range of applications from signal processing to video and image processing. Data-intensive structure and easy of implementation make Discrete Haar Wavelet Transform convenient to distribute fundamental operations to multi-CPU and multi-GPU systems. In this paper, the wavelet transform was ported in a compute-efficient way to CPU cluster and programmable GPU cluster by utilizing MPI and CUDA respectively. Experimental studies conducted as part of the parallelization strategies for two-dimensional Discrete Haar Wavelet Transform show that the total running time required to process all rows and columns of an image with different size is significantly decreased on the GPU cluster when compared to the its counterparts on a single CPU, single GPU and CPU cluster. Besides the speedup of the GPU based transform, preliminary analysis also showed that the size of the image is an important parameter on the scalability of the GPU cluster.