Advertisement

Gene Sequence Analysis of Breast Cancer Using Genetic Algorithm

  • Peyakunta BhargaviEmail author
  • Kanchi Lohitha LakshmiEmail author
  • Singaraju JyothiEmail author
Conference paper
  • 15 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)

Abstract

This paper describes the use of genetic algorithm (GA) implementation on tissue of breast cancer tumor and normal breast gene data sequences to analyze and discriminate among these data sequences. This discrimination is done between the breast cancer and non-breast cancer genetic factor data sequences based on optimal values generated after implementation of genetic algorithm in every single generation. Genetic algorithm is population-based evolutionary algorithm of soft computing techniques. Genetic algorithm utilizes arbitrary investigation of problem joined with evolutionary procedures like mutation (transformation) and crossover to improve the chance of predicting optimal solutions. GA provides different approaches to expand the chance of solving real-world genetic-related problems which enables to upgrade the execution of calculation. The main technique of GA is to produce possible guesses on provided information. The values calculated in the present work provide a way for progressive approach to design a framework to discriminate cancer and non-cancer data sequences.

Keywords

Genetic algorithm (GA) Soft computing (SC) Breast cancer diagnosis 

References

  1. 1.
    Radenbaugh, A.J.: Applications of genetic algorithms in bioinformatics, UMI Microform 1458165 Copyright 2008 by ProQuest LLC. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code, San Jose State UniversityGoogle Scholar
  2. 2.
    Parsons, R., Forrest, S., Burks, C.: Genetic Algorithms for DNA Sequence Assembly, From: ISMB-93 Proceedings. Copyright © 1993, AAAI (www.aaai.org). All rights reserved
  3. 3.
    Gupta, R., Agarwal, P., Soni, A.K.: Genetic algorithm based approach for obtaining alignment of multiple sequences. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 3(12) (2012)Google Scholar
  4. 4.
    Lakshmi, N.J., Gavarraju, P., Jeevana, J.K., Karteeka, P.: A literature survey on multiple sequence alignment algorithms. Int. J. Adv. Res. Comput. Sci. 6(3) (2016)Google Scholar
  5. 5.
    Ortuno, F., Valenzuela, O., Pomares, H., Rojas, I.: Determining the most suitable multiple sequence alignment methodology by using a set of heterogeneous biological features. In: IWBBIO 2013. Proceedings Granada, 18–20 Mar 2013Google Scholar
  6. 6.
    Lohitha Lakshmi, K., Rajesh, P.: An evolutionary optimization for multiple sequence alignment. IJCSN Int. J. Comput. Sci. Netw 3(4) (2014). ISSN (Online): 2277-5420 Impact Factor: 0.274, www.IJCSN.org
  7. 7.
    Jain, S.: Introduction to genetic algorithm & their application in data science, 31 July 2017Google Scholar
  8. 8.
    Aljahdali, S.H., Ghiduk, A.S., El-Telbany, M.: The Limitation of Genetic Algorithms in Software Testing, June 2010.  https://doi.org/10.1109/aiccsa.2010.5586984. Source IEEE Xplore
  9. 9.
    Naznin, F., Sarker, R., Essam, D.: Vertical decomposition with genetic algorithm for multiple sequence alignment. BMC Bioinform. 12(1), 353 (2011).  https://doi.org/10.1186/1471-2105-12-353CrossRefGoogle Scholar
  10. 10.
    Gondro, C., Kinghorn, B.P.: A simple genetic algorithm for multiple sequence alignment. Published 2007 in ISMB (2007)Google Scholar
  11. 11.
    Gondro, C., Kinghorn, B.P.: A simple genetic algorithm for multiple sequence alignment. Genet. Mol. Res. 6(4), 964–982 (2007). Received 3 Aug 2007, Accepted 25 Sept 2007, Published 5 Oct 2007Google Scholar
  12. 12.
    Ekmekci, B.: An Introduction to Programming for Bio scientists: A Python-Based Primer (2016)—Cited by 12—Related articles 7 June 2016Google Scholar
  13. 13.
    Multiple sequence alignment, From Wikipedia, the free encyclopaediaGoogle Scholar
  14. 14.
    Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan (1975)Google Scholar
  15. 15.
    El-Mihoub, T.A., Hopgood, A.A., Nolle, L., Battersby, A.: Hybrid genetic algorithms: a review. Eng. Lett. 13(2), EL_13_2_11 (Advance online publication: 4 August 2006Google Scholar
  16. 16.
    Fourment, M., Gillings, M.R.: A comparison of common programming languages used in bioinformatics. BMC Bioinform. 9, 82 (2008). Published online 2008 Feb 5.  https://doi.org/10.1186/1471-2105-9-82. PMCID: PMC2267699PMID: 18251993
  17. 17.
    Beasley, D., Bull, D.R., Martin, R.: An overview of genetic algorithms: part 1, fundamentals. Univ. Comput. 15, 58–69, 1993(3) (PDF) Hybrid Genetic Algorithms: A Review. Available from: [accessed Feb 15 2019]Google Scholar
  18. 18.
    Ahmed, Z.H.: An experimental study of a hybrid genetic algorithm for the maximum traveling salesman problem (2013).  https://doi.org/10.1186/2251-7456-7-10MathSciNetCrossRefGoogle Scholar
  19. 19.
    Abdesslem, L., Soham, M., Mohamed, B.: Multiple sequence alignment by quantum genetic algorithm. 1-4244-0054-6/06/$20.00 ©2006 IEEGoogle Scholar
  20. 20.
    Lohitha Lakshmi, K., Bhargavi, P., Jyothi, S.: An analysis of breast cancer dna sequences using particle swam optimization, Copyright © 2018 Authors This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Int. J. Eng. Technol. 7(4.7), 335–338 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSPMVVTirupatiIndia

Personalised recommendations