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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 3/2017

07.02.2017 | Regular Paper

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

verfasst von: Cristian Zambrano-Vega, Antonio J. Nebro, José García-Nieto, José F. Aldana-Montes

Erschienen in: Progress in Artificial Intelligence | Ausgabe 3/2017

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Abstract

Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a three-objective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones MOCell, GWASF-GA, and NSGA-III. Our study concludes that NSGA-II provides the best overall performance.
Nächster Artikel MR-DIS: democratic instance selection for big data by MapReduce

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Literatur
1.
Abbasi, M., Paquete, L., Pereira, F.: Local search for multiobjective multiple sequence alignment. In: Ortuño, F., Rojas, I. (eds.) Bioinformatics and Biomedical Engineering, Lecture Notes in Computer Science, vol. 9044, pp. 175–182. Springer, NewYork (2015)
2.
Bacon, D.J., Anderson, W.F.: Multiple sequence alignment. J. Mol. Biol. 191(2), 153–161 (1986)CrossRef
3.
Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., Bourne, P.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)CrossRef
4.
da Silva, F.J.M., Pérez, J.M.S., Pulido, J.A.G., Rodríguez, M.A.V.: Parallel niche pareto alineaga—an evolutionary multiobjective approach on multiple sequence alignment. J. Integr. Bioinf. 8(3), 174 (2011)
5.
Dayhoff, M., Schwartz, R., B.C. Orcutt, B.: A model of evolutionary change in proteins. In: Atlas of Protein Sequences and Structure 5, 345–352 (1978)
6.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
7.
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)CrossRef
8.
Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of non-parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)CrossRef
9.
Durillo, J.J., Nebro, A.J.: jMetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)CrossRef
10.
Edgar, R.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)CrossRef
11.
Handl, J., Kell, D., Knowles, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(2), 279–292 (2007)CrossRef
12.
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)CrossRef
13.
Kaya, M., Sarhan, A., Abdullah, R.: Multiple sequence alignment with affine gap by using multi-objective genetic algorithm. Comput. Methods Progr. Biomed. 114(1), 38–49 (2014)CrossRef
14.
Kemena, C., Taly, J., Kleinjung, J., Notredame, C.: Strike: evaluation of protein msas using a single 3d structure. Bioinformatics 27(24), 3385–3391 (2011)CrossRef
15.
Kukkonen, S., Deb, K.: Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In: IEEE International Conference on Evolutionary Computation, CEC 2006, part of WCCI 2006, Vancouver, BC, Canada, 16–21 July 2006, pp. 1179–1186 (2006)
16.
Lassmann, T., Sonnhammer, E.L.: Kalign—an accurate and fast multiple sequence alignment algorithm. BMC Bioinf. 6(1), 1–9 (2005)CrossRef
17.
Nebro, A., Durillo, J.J., Vergne, M.: Redesigning the jMetal multi-objective optimization framework. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO Companion ’15, pp. 1093–1100. ACM, New York, NY (2015)
18.
Nebro, A., Durillo, J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: a cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst. 24(7), 723–725 (2009)CrossRefMATH
19.
Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: a cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst. 24(7), 723–725 (2009)CrossRefMATH
20.
Ortuño, F., Valenzuela, O., Rojas, F., Pomares, H., Florido, J., Urquiza, J., Rojas, I.: Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns. Bioinformatics (Oxford, England) 29(17), 2112–2121 (2013)CrossRef
21.
Rani, R.R., Ramyachitra, D.: Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm. Biosystems 150, 177–189 (2016)CrossRef
22.
Rubio-Largo, A., Vega-Rodriguez, M., Gonzalez-Alvarez, D.: A hybrid multiobjective memetic metaheuristic for multiple sequence alignment. IEEE Trans. Evol. Comput. 99, 1–16 (2015)
23.
Rubio-Largo, A., Vega-Rodríguez, M., González-Álvarez, D.: Hybrid multiobjective artificial bee colony for multiple sequence alignment. Appl. Soft Comput. 41, 157–168 (2016)CrossRef
24.
Saborido, R., Ruiz, A.B., Luque, M.: Global WASF-GA: an evolutionary algorithm in multiobjective optimization to approximate the whole pareto optimal front. Evol. Comput. (2016) (In Press)
25.
Seeluangsawat, P., Chongstitvatana, P.: A multiple objective evolutionary algorithm for multiple sequence alignment. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 477–478. ACM, New York, NY (2005)
26.
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, Boca Raton (2007)MATH
27.
Soto, W., Becerra, D.: A multi-objective evolutionary algorithm for improving multiple sequence alignments. In: Campos. S. (ed.) Advances in Bioinformatics and Computational Biology. Lecture Notes in Computer Science, vol. 8826, pp. 73–82. Springer, NewYork (2014)
28.
Thompson, J., Koehl, P., Poch, O.: Balibase 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61, 127–136 (2005)CrossRef
29.
Van Walle, I., Lasters, I., Wyns, L.: Sabmarka benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21(7), 1267–1268 (2005)CrossRef
30.
Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1(4), 337–348 (1994)CrossRef
31.
Zhu, H., He, Z., Jia, Y.: A novel approach to multiple sequence alignment using multiobjective evolutionary algorithm based on decomposition. IEEE J. Biomed. Health Inf. 20(2), 717–727 (2016)CrossRef
32.
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)CrossRef
33.
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRef
Metadaten
Titel
Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment
verfasst von
Cristian Zambrano-Vega
Antonio J. Nebro
José García-Nieto
José F. Aldana-Montes
Publikationsdatum
07.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 3/2017
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-017-0116-6

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