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A Multiobjective Variable Neighborhood Search for Solving the Motif Discovery Problem

  • David L. González-Álvarez
  • Miguel A. Vega-Rodríguez
  • Juan A. Gómez-Pulido
  • Juan M. Sánchez-Pérez
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

In this work we approach the Motif Discovery Problem (MDP) by using a trajectory-based heuristic. Identifying common patterns, motifs, in deoxyribonucleic acid (DNA) sequences is a major problem in bioinformatics, and it has not yet been resolved in an efficient manner. The MDP aims to discover patterns that maximize three objectives: support, motif length, and similarity. Therefore, the use of multiobjective evolutionary techniques can be a good tool to get quality solutions. We have developed a multiobjective version of the Variable Neighborhood Search (MO-VNS) in order to handle this problem. After accurately tuning this algorithm, we also have implemented its variant Multiobjective Skewed Variable Neighborhood Search (MO-SVNS) to analyze which version achieves more complete solutions. Moreover, in this work, we incorporate the hypervolume indicator, allowing future comparisons of other authors. As we will see, our algorithm achieves very good solutions, surpassing other proposals.

Keywords

Pareto Front Transcription Factor Binding Site Variable Neighborhood Variable Neighborhood Search Position Weight Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Stine, M., Dasgupta, D., Mukatira, S.: Motif discovery in upstream sequences of coordinately expressed genes. In: The 2003 Congress on Evolutionary Computation (CEC 2003), December 2003, vol. 3, pp. 1596–1603 (2003)Google Scholar
  2. 2.
    Kaya, M.: Motif discovery using multi-objective genetic algorithm in biosequences. In: Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 320–331. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers and Operations Research 24, 1097–1100 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Fonseca, C.M., Paquete, L., Lopez–Ibanez, M.: An improved dimension–sweep algorithm for the hypervolume indicator. In: IEEE Congress on Evolutionary Computation (CEC 2006), July 2006, pp. 1157–1163 (2006)Google Scholar
  5. 5.
    Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1), 137–144 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Wingender, E., Dietze, P., Karas, H., Knüppel, R.: TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Research 24(1), 238–241 (1996)CrossRefGoogle Scholar
  7. 7.
    Kaya, M.: MOGAMOD: Multi–objective genetic algorithm for motif discovery. Expert Systems with Applications: An International Journal 36(2), 1039–1047 (2009)CrossRefGoogle Scholar
  8. 8.
    Roth, F.P., Hughes, J.D., Estep, P.W., Church, G.M.: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole genome mRNA quantitation. Nature Biotechnology 16(10), 939–945 (1998)CrossRefGoogle Scholar
  9. 9.
    Bailey, T.L., Elkan, C.: Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Machine Learning 21(1-2), 51–80 (1995)CrossRefGoogle Scholar
  10. 10.
    Pavesi, G., Mereghetti, P., Mauri, G., Pesole, G.: Weeder Web: discovery of transcription factor binding sites in a set of sequences from co–regulared genes. Nucleic Acids Research 32, 199–203 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David L. González-Álvarez
    • 1
  • Miguel A. Vega-Rodríguez
    • 1
  • Juan A. Gómez-Pulido
    • 1
  • Juan M. Sánchez-Pérez
    • 1
  1. 1.Polytechnic SchoolUniversity of ExtremaduraCáceresSpain

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