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Multiobjective optimization algorithms for motif discovery in DNA sequences

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Abstract

Optimization techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have been used to address an important biological problem known as the motif discovery problem. As it is defined as a multiobjective optimization problem, we have adapted the proposed algorithms to this optimization context. We evaluate the proposed metaheuristics on 54 sequence datasets that belong to four organisms with different numbers of sequences and sizes. The results have been analysed in order to discover which algorithm performs best in each case. The algorithms implemented and the results achieved can assist biological researchers in the complicated task of finding DNA patterns with an important biological relevance.

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Acknowledgments

This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the contract TIN2012-30685 (BIO project). David L. González-Álvarez is supported by the postdoc research grant ACCION-III-15 from the University of Extremadura.

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Authors and Affiliations

  1. Department Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, Escuela Politécnica, Campus Universitario s/n, 10003, Cáceres, Spain

    David L. González-Álvarez, Miguel A. Vega-Rodríguez & Álvaro Rubio-Largo

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  1. David L. González-Álvarez
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Correspondence to David L. González-Álvarez.

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González-Álvarez, D.L., Vega-Rodríguez, M.A. & Rubio-Largo, Á. Multiobjective optimization algorithms for motif discovery in DNA sequences. Genet Program Evolvable Mach 16, 167–209 (2015). https://doi.org/10.1007/s10710-014-9232-2

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