An Analysis of Breast Cancer DNA Sequences Using Particle Swam Optimization

  • Authors

    • K. Lohitha Lakshmi
    • P. Bhargavi
    • S. Jyothi
    2018-09-27
    https://doi.org/10.14419/ijet.v7i4.7.20572
  • PSO, Soft Computing Techniques, Breast Cancer, Diagnosis, Analysis, Prognosis.

  • Abstract

    Conceptual Breast tumour conclusion, examination, and visualization are essential research challenges in Bioinformatics. Bosom tumour analysis incorporates recognizing of malignancy bumps and ordinary tissue. Investigation incorporates the present phase of the malignancy tissue and anticipation incorporates expectation of repeat of the bosom tumour in future ages in light of structure and game plan of the individual DNA succession. This paper investigations bosom disease DNA succession to anticipate event of bosom tumour utilizing Particle Swarm Optimization (PSO).PSO procedure is a populace based pursuit calculation that mirrors the social conduct of swam. As the piece of investigation of bosom disease in human, the DNA arrangements of ordinary bosom tissue are contrasted and DNA groupings of bosom tumour tissue utilizing PSO... The distinction between the ordinary and breast cancer disease DNA sequences are broke down in view of the summarized values generated by applying PSO algorithm.

     

  • References

    1. [1] Dogan Ibrahim, “An overview of soft computingâ€, 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29-30 August 2016, Vienna, Austria, Procedia Computer Science 102 ( 2016 ) 34 – 38, Science Direct.

      [2] Analyn Salaria,†Breast Cancerâ€, Malabog National High Schoolsalvacion, Daraga, Albay, A RESEARCH PAPER IN ENGLISH 1V.

      [3] Web reference: https://www.customwritings.com/blog/sample-research-papers/research-paper-breast-cancer.html

      [4] Nature Inspired Computation Techniques and Its Applications in Soft Computing: Survey K. Himabindu1 , S. Jyothi2 1, 2Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam (Women’s University), Tirupati, INDIA, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 5 Issue VII, July 2017- Available at www.ijraset.com

      [5] K. Bhargavi* and S. Jyothi,†Classification of DNA Sequence Using Soft Computing Techniques: A Surveyâ€, Indian Journal of Science and Technology, Vol 9(47), DOI: 10.17485/ijst/2016/v9i47/89343, December 2016 ISSN (Print): 0974-6846 ISSN (Online): 0974-5645.

      [6] Neelam Goel, Shailendra Singh, and Trilok Chand Aseri, “A Review of Soft Computing Techniques for Gene Predictionâ€, ISRN Genomics, Volume 2013, Article ID 191206, 8 pages, http://dx.doi.org/10.1155/2013/19120666.

      [7] A. B. Kurhe, S. S. Satonkar, P. B. Khanale, and S. Ashok, “Soft computing and its applications,†BIOINFO Soft Computing, vol. 1, pp. 5–7, 2011. View at Google Scholar.

      [8] S. Rajasekaran and G. A. V. Pai, Neural Network, Fuzzy Logic and Genetic Algorithms- Synthesis and Applications, Prentice-Hall, 2005.

      [9] Webreference:https://www.cancer.gov/about-cancer/treatment/types/precision-medicine/ tumour-dna-sequencing.

      [10] RongMaJianpingGongXiaoweiJiang, “Novel applications of next-generation sequencing in breast cancer researchâ€, Genes & Diseases, volume 4, Issue 3, September 2017, Pages 149-153,open access.

      [11] Kokichi Sugano,Seigo Nakamura ,Jiro Ando ,†crossâ€sectional analysis of germ line BRCA1 and BRCA2 mutations in Japanese patients suspected to have hereditary breast/ovarian cancerâ€, 23oct2008.

      [12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,†in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43.

      [13] K. LohithaLakshmi, P.Bhargavi, S.Jyothi,†Soft Computing Techniques for Gene Annotationâ€, IJLEMR, ISSN:2455-4847,Volume 3-Issue 2,February 2018,PP 26-34.

      [14] J. Kennedy and R. Eberhart, “Particle swarm optimization,†in Proceedings of IEEE International Conference on Neural Networks (ICNN), 1995, pp. 1942–1948.

      [15] R. Eberhart and Y. Shi, “Particle swarm optimization: Developments, applications and resources,†in Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 2001, pp. 81–86.

      [16] S. Cheng, Y. Shi, and Q. Qin, “Population diversity based study on search information propagation in particle swarm optimizationâ€, in Proceedings of 2012 IEEE Congress on Evolutionary Computation, (CEC 2012). Brisbane, Australia: IEEE, 2012, pp. 1272–1279.

      [17] Vijaylakshmi S and Priyadarshini J,†An Analysis of Particle Swarm Optimization Technique for Breast Cancer Datasetâ€, I J C T A, 9(3), 2016, pp. 297-308, © International Science Press.

      [18] Ravi Shankar Verma, Vikas Singh, Sanjay Kumar,†DNA Sequence Assembly using Particle Swarm Optimizationâ€, International Journal of Computer Applications (0975 – 8887), Volume 28– No.10, August 2011.

      [19] Web ref: https://www.freelancinggig.com/blog/2017/07/19/best-programming-languages-bioinformatics/

      [20] Hans Petter Langtangen, Geir Kjetil Sandveâ€, Illustrating Python via Bioinformatics Examplesâ€, Center for Biomedical Computing, Simula Research Laboratory, Department of Informatics, University of Oslo, Mar 22, 2015.

      [21] Web ref: https://www.thoughtco.com / dna-versus-rna-608191

      [22] Web ref: https://en.wikipedia.org/wiki/ Messenger_RNA.

      [23] Suzanne Clancy, Ph.D. & William Brown, Ph.D. (Write Science Right) © 2008 Nature Education

      [24] Citation: Clancy, S. & Brown, W. (2008) Translation: DNA to mRNA to Protein. Nature Education 1(1): 101.

  • Downloads

  • How to Cite

    Lohitha Lakshmi, K., Bhargavi, P., & Jyothi, S. (2018). An Analysis of Breast Cancer DNA Sequences Using Particle Swam Optimization. International Journal of Engineering & Technology, 7(4.7), 335-338. https://doi.org/10.14419/ijet.v7i4.7.20572

    Received date: 2018-09-29

    Accepted date: 2018-09-29

    Published date: 2018-09-27