Abstract
The Multiobjective Artificial Bee Colony with Differential Evolution (MO-ABC/DE) is a new hybrid multiobjective evolutionary algorithm proposed for solving optimization problems. One important optimization problem in Bioinformatics is the Motif Discovery Problem (MDP), applied to the specific task of discovering DNA patterns (motifs) with biological significance, such as DNA-protein binding sites, replication origins or transcriptional DNA sequences. In this work, we apply the MO-ABC/DE algorithm for solving the MDP using as benchmark genomic data belonging to four organisms: drosophila melanogaster, homo sapiens, mus musculus, and saccharomyces cerevisiae. To demonstrate the good performance of our algorithm we have compared its results with those obtained by four multiobjective evolutionary algorithms, and their predictions with those made by thirteen well-known biological tools. As we will see, the proposed algorithm achieves good results from both computer science and biology point of views.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Che, D., Song, Y., Rashedd, K.: MDGA: Motif discovery using a genetic algorithm. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), pp. 447–452 (2005)
Congdon, C.B., Fizer, C.W., Smith, N.W., Gaskins, H.R., Aman, J., Nava, G.M., Mattingly, C.: Preliminary results for GAMI: A genetic algorithms approach to motif inference. In: Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005), pp. 97–104 (2005)
Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
D’haeseleer, P.: What are DNA sequence motifs? Nature Biotechnology 24(4), 423–425 (2006)
Fogel, G.B., et al.: Evolutionary computation for discovery of composite transcription factor binding sites. Nucleic Acids Research 36(21), 1–14 (2008)
Fogel, G.B., Weekes, D.G., Varga, G., Dow, E.R., Harlow, H.B., Onyia, J.E., Su, C.: Discovery of sequence motifs related to coexpression of genes using evolutionary computation. Nucleic Acids Research 32(13), 3826–3835 (2004)
González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Predicting DNA motifs by using evolutionary multiobjective optimization. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 42(6), 913–925 (2011)
González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery. Engineering Applications of Artificial Intelligence 26(1), 314–326 (2012)
Grosan, C., Abraham, A.: Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms. SCI, vol. 75, pp. 1–17. Springer, Heidelberg (2007)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Turkey (2005)
Kaya, M.: MOGAMOD: Multi-objective genetic algorithm for motif discovery. Expert Systems with Applications 36(2), 1039–1047 (2009)
Liu, F.F.M., Tsai, J.J.P., Chen, R.M., Chen, S.N., Shih, S.H.: FMGA: finding motifs by genetic algorithm. In: Fourth IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2004), pp. 459–466 (2004)
Lones, M.A., Tyrrell, A.M.: Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(3), 403–414 (2007)
Paul, T.K., Iba, H.: Identification of weak motifs in multiple biological sequences using genetic algorithm. In: Proceedings of the 2006 Conference on Genetic and Evolutionary Computation (GECCO 2006), pp. 271–278 (2006)
Rubio-Largo, A., González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: MO-ABC/DE - multiobjective artificial bee colony with differential evolution for unconstrained multiobjective optimization. In: 13th IEEE International Symposium on Computational Intelligence and Informatics, pp. 157–162 (2012)
Shao, L., Chen, Y.: Bacterial foraging optimization algorithm integrating tabu search for motif discovery. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2009), pp. 415–418 (2009)
Stine, M., Dasgupta, D., Mukatira, S.: Motif discovery in upstream sequences of coordinately expressed genes. In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 3, pp. 1596–1603 (2003)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1), 137–144 (2005)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 8(2), 173–195 (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical report tik-report 103, Swiss Federal Institute of Technology, Zurich, Switzerland (2001)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
González-Álvarez, D.L., Vega-Rodríguez, M.A. (2013). Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-37189-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37188-2
Online ISBN: 978-3-642-37189-9
eBook Packages: Computer ScienceComputer Science (R0)