Citation: Saeed Alirezanejad Gohardani, Mehri Bagherian, Hamidreza Vaziri. A multi-objective imperialist competitive algorithm (MOICA) for finding motifs in DNA sequences[J]. Mathematical Biosciences and Engineering, 2019, 16(3): 1575-1596. doi: 10.3934/mbe.2019075
[1] | F. Zare-Mirakabad, H. Ahrabian and M. Sadeghi, et al., Genetic algorithm for dyad pattern finding in DNA sequences, Genes Genet. Syst., 84 (2009), 81–93. |
[2] | M. Li, B. Ma and L. Wang, Finding similar regions in many sequences, J. Comput. Syst. Sci., 65 (2002), 73–96. |
[3] | M. F. Sagot, Spelling approximate repeated or common motifs using a suffix tree, Springer, 1998. |
[4] | F. W. Glover and G. A. Kochenberger, Handbook of metaheuristics, Springer Science & Business Media, 2006. |
[5] | E. Czeizler, T. Hirvola and K. Karhu, A graph-theoretical approach for motif discovery in protein sequences, IEEE/ACM Trans. Comput. Biol. Bioinf., 14 (2017), 121–130. |
[6] | M. Kaya, MOGAMOD: Multi-objective genetic algorithm for motif discovery, Expert. Syst. Appl., 36 (2009), 1039–1047. |
[7] | D. L. González-Álvarez, M. A. Vega-Rodríguez and Á. Rubio-Largo, Multiobjective optimization algorithms for motif discovery in DNA sequences, Genet. Program. Evolvable Mach., 16 (2015), 167–209. |
[8] | C. E. Lawrence and A. A. Reilly, An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences, Proteins, 7 (1990), 41–51. |
[9] | C. E. Lawrence, S. F. Altschul and M. S. Boguski, et al., Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment, Science, 262 (1993), 208–214. |
[10] | T. L. Bailey and C. Elkan, Fitting a mixture model by expectation maximization to discover motifs in bipolymers, Proc. Int. Conf. Intell. Syst. Mol. Biol., 2 (1994), 28–36.. |
[11] | F. P. Roth, J. D. Hughes and P. W. Estep, et al., Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation, Nat. Biotechnol., 16 (1998), 939–945. |
[12] | K. C. Wong, MotifHyades: Expectation maximization for de novo DNA motif pair discovery on paired sequences, Bioinformatics, 33 (2017), 3028–3035. |
[13] | K. C. Wong, DNA Motif Recognition Modeling from Protein Sequences, iScience, 7 (2018), 198–211. |
[14] | G. Pavesi, P. Mereghetti and G. Mauri, et al., Weeder Web: Discovery of transcription factor binding sites in a set of sequences from co-regulated genes. Nucleic Acids Res., 32 (2004), W199–W203. |
[15] | E. Eskin and P. A. Pevzner, Finding composite regulatory patterns in DNA sequences, Bioinformatics, 18 (2002), S354–S363. |
[16] | P. A. Evans and A. D. Smith, Toward optimal motif enumeration, Springer, 2003. |
[17] | J. Serra, A. Matic and A. Karatzoglou, et al., A genetic algorithm to discover flexible motifs with support, IEEE, 2016. |
[18] | N. Pisanti, A. M. Carvalho and L. Marsan, et al., RISOTTO: Fast extraction of motifs with mismatches, Springer, 2006. |
[19] | G. Z. Hertz and G. D. Stormo, Identifying DNA and protein patterns with statistically significant alignments of multiple sequences, Bioinformatics, 15 (1999), 563–577. |
[20] | D. L. González-Álvarez, M. A. Vega-Rodríguez and J. A. Gómez-Pulido, et al., Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm, Springer, 2011. |
[21] | D. L. González-Álvarez, M. A. Vega-Rodríguez and Á. Rubio-Largo, Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm, Parallel. Comput., 76 (2018), 1–17. |
[22] | E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE T. Evolut. Comput., 3 (1999), 257–271. |
[23] | E. Wingender, P, Dietze and H. Karas, et al., TRANSFAC: A database on transcription factors and their DNA binding sites, Nucleic Acids Res., 24 (1996), 238–241. |
[24] | D. L. González-Álvarez, M. A. Vega-Rodríguez and J. A. Gómez-Pulido, et al., Solving the motif discovery problem by using differential evolution with pareto tournaments, IEEE, 2010. |
[25] | G. B. Fogel, D. G. Weekes and G. Varga, et al., Discovery of sequence motifs related to coexpression of genes using evolutionary computation, Nucleic Acids Res., 32 (2004), 3826–3835. |
[26] | E. Zitzler, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results, Evolut. Comput., 8 (2000), 173–195. |
[27] | E. Atashpaz-Gargari and C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE, 2007. |
[28] | D. L. Gonzalez-Álvarez, M. A. Vega-Rodriguez and J. A. Gomez-Pulido, et al., Predicting DNA motifs by using evolutionary multiobjective optimization, IEEE. T. Syst. Man. Cy. C., 42 (2012), 913–925. |
[29] | X. S. Yang, Firefly algorithms for multimodal optimization, Springer, 2009. |
[30] | D. L. González-Álvarez, M. A. Vega-Rodríguez and J, A. Gómez-Pulido, et al., Applying a multiobjective gravitational search algorithm (MO-GSA) to discover motifs, Springer, 2011. |
[31] | E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm, 2001. |
[32] | K. Deb, A. Pratap and S. Agarwal, et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evolut. Comput., 6 (2002), 182–197. |
[33] | M. Tompa, N. Li and T. L. Bailey, et al., Assessing computational tools for the discovery of transcription factor binding sites, Nat. Biotechnol., 23 (2005), 137. |