Gene Selection for Diagnosis of Cancer in Microarray Data Using Memetic Algorithm

  • Shemim Begum
  • Souravi Chakraborty
  • Abakash Banerjee
  • Soumen Das
  • Ram Sarkar
  • Debasis Chakraborty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Selecting a small subset of genes that helps to build a good classification model for prediction of disease on the microarray data is a very demanding optimization problem. Genetic algorithm (GA) is a population-based optimization algorithm, which has a lot of applications in the field of molecular biology. But the premature convergence is one of the limitations of GA. Memetic algorithm (MA), an extension of GA, diminishes the possibility of such premature convergence. Microarray technology enables to measure the expression level of thousands of genes to recognize the changes in expression level among different biological states. In this paper, superiority of MA is established over GA, simulated annealing (SA), and tabu search (TS), while selecting the genes in microarray data. Experiments on three well-known data sets, namely DLBCL, leukemia, and prostate cancer, exhibit that MA yields more promising results than classical GA, SA, and TS.

Keywords

Memetic algorithm Symmetrical uncertainty Microarray data Gene selection 

References

  1. 1.
    Ruskin, J.: Computational modeling and analysis of microarray data. Microarrays (Basel) 5(4), 26 (2016)CrossRefGoogle Scholar
  2. 2.
    Keogh, E., Mueen, A: Curse of dimensionality. Encycl. Mach. Learn., 257–258 (2010)Google Scholar
  3. 3.
    Garg, P., et al.: A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm. In: Inter. J. Net. Sec. & Its App. (IJNSA) 1(1), (2009)Google Scholar
  4. 4.
    Duval, B., Hao, J.-K., Hernandez Hernandez, J.C.: A memetic algorithm for gene selection and molecular classification of cancer. In: GECCO’09, July 8–12, Montréal Québec, Canada (2009)Google Scholar
  5. 5.
    Dash, R., Misra, B.: Gene selection and classification of microarray data: a Pareto DE approach. Intell. Decis. Technol. 11(1), 1–15 (2016)Google Scholar
  6. 6.
    Ayadi, W., Hao, J.K.: A memetic algorithm for discovering negative correlation biclusters of DNA microarray data. Neurocomputing 145, 14–22 (2014)CrossRefGoogle Scholar
  7. 7.
    Cotta, C., Moscato, P., Garcia, V., Frana, P., Mendes, A.: Gene ordering in microarray data using parallel memetic algorithms. In: 2012 41st International Conference on Parallel Processing Workshops Oslo, Norway, June (2005)Google Scholar
  8. 8.
    Duval, B., Hao, J.K.: Advances in metaheuristics for gene selection and classification of microarray data. Brief. Bioinform. 11(1), 127–141 (2010)CrossRefGoogle Scholar
  9. 9.
    Sekhara Rao, A.C., Dara, S., Haider, B.: Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI J., 460–473 (2016)Google Scholar
  10. 10.
    Gautam, A., et al.: An improved mammogram classification approach using back propagation neural network. In: Proceedings of the 3rd International Conference on Computer and Communication Technology (IC3T-2016). Springer (2016)Google Scholar
  11. 11.
    Luke, S., Spector, L.: A comparison of crossover and mutation in genetic programming. In: Koza, J., et al. (eds.) Proceedings of the Second Annual Conference on Genetic Programming (GP-97). Morgan Kaufmann (1997)Google Scholar
  12. 12.
    Huang, J., Huang, N.: A method for feature selection based on the correlation. In: International Conference on Measurement, Information and Control (MIC) (2012)Google Scholar
  13. 13.
    Umbarkar, A.J., Sheth, P.D.: Crossover operator in genetic algorithm: a review. ICTACT J. Soft Comput. 6(01) (2015)Google Scholar
  14. 14.
  15. 15.
    Byun, H., Lee, S.W.: Applications of support vector machines for pattern recognition: a survey. In: SVM 2002. LNCS, vol. 2388, pp. 213–236. Springer, Berlin (2002)CrossRefGoogle Scholar
  16. 16.
    Sarhrouni, E., Hammouch, A., Aboutajdine, D.: Application of symmetric uncertainty and mutual information to dimensionality reduction and classification of hyperspectral images. Int. J. Eng. Technol. (IJET) 4(5) (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Shemim Begum
    • 1
  • Souravi Chakraborty
    • 1
  • Abakash Banerjee
    • 1
  • Soumen Das
    • 1
  • Ram Sarkar
    • 2
  • Debasis Chakraborty
    • 3
  1. 1.Government College of Engineering & Textile TechnologyBerhampore, MurshidabadIndia
  2. 2.Jadavpur UniversityKolkataIndia
  3. 3.Murshidabad College of Engineering and TechnologyBerhampore, MurshidabadIndia

Personalised recommendations