Abstract
In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept of SOM is utilized in designing new genetic operators for both gene and condition clusters to reach to the optimal solution in a faster way. In order to measure the goodness of a bi-clustering solution, three bi-cluster quality measures, mean squared error, row variance, and bi-cluster size, are optimized simultaneously using differential evolution as the underlying optimization strategy. The concept of polynomial mutation is incorporated in our framework to generate highly diverse solutions which in turn helps in faster convergence. The proposed approach is applied on two real-life microarray gene expression datasets and results are compared with various state-of-the-art techniques. Results obtained clearly illustrate that our approach extracts high-quality bi-clusters as compared to other methods and also it converges much faster than other competitors. Further, the obtained results are validated using statistical significance test and biological significance test.
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References
Acharya S, Saha S, Sahoo P (2019) Bi-clustering of microarray data using a symmetry-based multi-objective optimization framework. Soft Comput 23(14):5693–5714
Angiulli F, Pizzuti C (2005) Gene expression biclustering using random walk strategies. In: International Conference on Data Warehousing and Knowledge Discovery. Springer, pp 509– 519
Bandyopadhyay S, Saha S, Maulik U, Deb K (2008) A simulated annealing-based multiobjective optimization algorithm: Amosa. IEEE Trans Evol Comput 12(3):269–283
Ben-Dor A, Chor B, Karp R, Yakhini Z (2003) Discovering local structure in gene expression data: the order-preserving submatrix problem. J Comput Biol 10(3-4):373–384
Bousselmi M, Bechikh S, Hung CC, Said LB (2017) Bi-mock: A multi-objective evolutionary algorithm for bi-clustering with automatic determination of the number of bi-clusters. In: International Conference on Neural Information Processing. Springer, pp 366–376
Chakraborty A, Maka H (2005) Biclustering of gene expression data using genetic algorithm. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB’05. Proceedings of the 2005. IEEE, pp 1–8
Cheng Y, Church GM (2000) Biclustering of expression data. In: Ismb, vol 8, pp 93–103
Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern-Part A: Syst Hum 38(1):218–237
Deb K, Tiwari S (2008) Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization. Eur J Oper Res 185(3):1062–1087
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197
Divina F, Aguilar-Ruiz JS (2007) A multi-objective approach to discover biclusters in microarray data. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. ACM, pp 385–392
Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci 97(22):12,079–12,084
Hartigan JA (1972) Direct clustering of a data matrix. J Amer Stat Assoc 67(337):123–129
Ihmels J, Bergmann S, Barkai N (2004) Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13):1993–2003
Jain AK, Dubes RC (1988) Algorithms for clustering data. Inc, Prentice-Hall
Jia Y, Li Y, Liu W, Dong H (2016) An efficient weighted biclustering algorithm for gene expression data. In: 2016 17Th international conference on parallel and distributed computing, applications and technologies (PDCAT). IEEE, pp 336–341
Kohonen T (1998) The self-organizing map. Neurocomputing 21(1):1–6
Maulik U, Mukhopadhyay A, Bandyopadhyay S (2009) Finding multiple coherent biclusters in microarray data using variable string length multiobjective genetic algorithm. IEEE Trans Inf Technol Biomed 13(6):969–975
Saini N, Chourasia S, Saha S, Bhattacharyya P (2017) A self organizing map based multi-objective framework for automatic evolution of clusters. In: International Conference on Neural Information Processing. Springer, pp 672–682
Seridi K, Jourdan L, Talbi EG (2015) Using multiobjective optimization for biclustering microarray data. Appl Soft Comput 33:239–249
Suresh K, Kundu D, Ghosh S, Das S, Abraham A (2009) Data clustering using multi-objective differential evolution algorithms. Fund Inf 97(4):381–403
Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(suppl_1):S136–S144
Teng L, Chan LW (2006) Biclustering gene expression profiles by alternately sorting with weighted correlated coefficient. In: 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, pp 289–294
Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE Congress on evolutionary computation, vol 2, pp 1980–1987
Welch BL (1947) The generalization of ‘student’s’ problem when several different population variances are involved, vol 34. http://www.jstor.org/stable/2332510
Yang J, Wang H, Wang W, Yu P (2003) Enhanced biclustering on expression data. In: Proceedings. 2003. Third IEEE Symposium on Bioinformatics and bioengineering. IEEE, pp 321–327
Zhang D, Wei B (2014) Comparison between differential evolution and particle swarm optimization algorithms. In: 2014 IEEE International Conference on Mechatronics and automation (ICMA). IEEE, pp 239–244
Zhang H, Zhang X, Gao XZ, Song S (2016a) Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble, vol 173
Zhang H, Zhou A, Song S, Zhang Q, Gao XZ, Zhang J (2016b) A self-organizing multiobjective evolutionary algorithm. IEEE Trans Evol Comput 20(5):792–806
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Dr. Sriparna Saha would like to acknowledge the support of SERB Women in Excellence Award-SB/WEA-08/2017 for conducting this research.
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Saini, N., Saha, S., Soni, C. et al. Automatic evolution of bi-clusters from microarray data using self-organized multi-objective evolutionary algorithm. Appl Intell 50, 1027–1044 (2020). https://doi.org/10.1007/s10489-019-01554-w
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DOI: https://doi.org/10.1007/s10489-019-01554-w