Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm

González-Álvarez, David L.; Vega‐Rodríguez, Miguel A.; Gómez–Pulido, Juan A.; Sánchez‐Pérez, Juan M.

doi:10.1007/978-3-642-20389-3_9

Cited by 22 publications

(8 citation statements)

References 16 publications

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“…For fair comparison with other metaheuristics, population or colony size and the number of maximum cycles or generations, which are common control parameters and directly determine the total number of evaluations, were chosen to be 200 and 3000, respectively (Kaya, 2007;Gonzalez-Alvarez et al, 2010, 2011a. In order to make a more detailed investigation of the proposed discrete model based on similarity between consensus sequences of the ABC algorithm, we used six different values for the limit parameter: 50, 100, 200, 300, 500, and a.…”

Section: Resultsmentioning

confidence: 99%

“…In order to make a more detailed investigation of the proposed discrete model based on similarity between consensus sequences of the ABC algorithm, we used six different values for the limit parameter: 50, 100, 200, 300, 500, and a. For each data set and the selected motif length, 30 independent runs were carried out with different random seeds (Kaya, 2007;Gonzalez-Alvarez et al, 2010, 2011a. The highest and mean similarity values and standard deviations of the 30 runs for each combination of parameters are given in Tables 1-3 for the hm03r, yst04r, and yst08r data sets.…”

Section: Resultsmentioning

confidence: 99%

“…From the results given in Tables 1-3, it can be clearly seen that the consensus ABC algorithm produces more efficient results, in terms of mean and highest similarity values, when the limit parameter value is set to 50. The similarity values of the consensus ABC algorithm with the support values set to equal the number of sequences in each data set were calculated utilizing Equation 1 (Kaya, 2007;Gonzalez-Alvarez et al, 2010, 2011a) and compared with the MOGOMOD (Kaya, 2007), DEPT (Gonzalez-Alvarez et al, 2010) and MOABC (Gonzalez-Alvarez et al, 2011a) algorithms. For the MOGAMOD, DEPT, and MOABC algorithms, each experiment was performed 30 times with the given population and maximum number of cycles and the final Pareto front was created by combining the Pareto fronts found in all 30 independent runs.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Karaboğa¹,

Aslan²

2016

Genet. Mol. Res.

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ABSTRACT. 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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Karaboğa¹,

Aslan²

2016

Genet. Mol. Res.

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“…In the adaptive BBO algorithm, the parameters of modification probability and mutation probability are altered according to Equations (7) and (8). In Equations (7) and (8), constant factor k 1 , k 2 , k 3 and k 4 which range between 0 and 1 are set by users.…”

Section: Adaptive Bbo For Mdpmentioning

confidence: 99%

Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

2017

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Abstract:The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.

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“…Finding motifs in DNA sequences applying a multi objective ABC algorithm was presented by Vega Rodriguez et al [34]. Motif has major application in the specific task of discovering transcription factor binding sites (TFBS) in DNA sequences but motif discovery is an NP-hard problem and to solve this problem author(s) proposed a multi-objective ABC algorithm (MOABC).…”

Section: Bioinformatics Fieldmentioning

confidence: 99%

A review on Artificial Bee Colony algorithm

Verma

Kumar

2013

IJET

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In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

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Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm

Cited by 22 publications

References 16 publications

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

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

Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

A review on Artificial Bee Colony algorithm

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