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A Novel Clustering-Based Gene Expression Pattern Analysis for Human Diabetes Patients Using Intuitionistic Fuzzy Set and Multigranulation Rough Set Model

  • Swarup Kr GhoshEmail author
  • Anupam Ghosh
Conference paper
  • 32 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)

Abstract

In this article, we present an intuitionistic fuzzy set (IFS)-based gene expression pattern classification using multigranulation rough set theory for human diabetes patient. At the very beginning, the proposed scheme generates a soft-information structure from the microarray by IFS via multiple fuzzy membership functions with Yager generating function. The intuitionistic fuzzy set deals with the ambiguity between normal state and diabetic state from gene expression microarray via the hesitation degree while shaping the membership function. Thereafter, a multigranulation rough set is utilized for the measurement of accuracy and roughness from expression pattern that has been deferentially expressed from normal state to diabetic state. Lastly, Rough-fuzzy C-means clustering has been applied on the datasets into two clusters such as diabetes or non-diabetes. The associations among human genes have also been identified which are correlated with diabetes (type-2). In addition, we have validated a measurement by F-score using diabetes gene expression NCBI database and achieved better performance in comparison with baseline methods.

Keywords

Diabetes gene microarray Rough set Multigranulation Intuitionistic fuzzy set Rough-fuzzy c-means 

References

  1. 1.
    Rebecca, T.L.: Prevalence of diabetic retinopathy within a national diabetic retinopathy screening service. British J. Ophthal. 99(1), 64–68 (2015)CrossRefGoogle Scholar
  2. 2.
    Hanson, R.L., Bogardus, C., Duggan, D.: A search for variants associated with young-onset type 2 diabetes in Americal Indians in 100K genotyping array. Diabetes 56(12), 3045–3052 (2007)CrossRefGoogle Scholar
  3. 3.
    Stekel, D.: Microarray Bioinformatics. Cambridge University Press, Cambridge (2006)Google Scholar
  4. 4.
    Das, R., Kalita, J., Bhattacharyya, D.K.: A pattern matching approach for clustering gene expression data. Int. J. Data Mining Modeling Manage. 3(2) (2011)Google Scholar
  5. 5.
    Jiang, D., Peri, J. Zhang, A.: DHC: a density based hierarchical clustering methods for time series gene expression data. In: IEEE International Symposium on Bioinformatics and Bioengineering (2003)Google Scholar
  6. 6.
    Banerjee, M., Mitra, S., Banka, H.: Evolutionary rough feature selection in gene expression data. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 37 (2007)Google Scholar
  7. 7.
    Kalaiselvi, N., Inbarani, H.: A fuzzy soft set based classification for gene expression data. IJSER 3, 1315–1321 (2014)Google Scholar
  8. 8.
    Danaee, P., Hendrix D.A.: A deep learning approach for cancer detection and relevant gene identification. In: Pacific Symposium on Biocomputing, pp. 219–229 (2017)Google Scholar
  9. 9.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973). : https://doi.org/10.1080/01969727308546046 ISSN: 0022-0280
  10. 10.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. ISBN 0-306-40671-3 (1981)Google Scholar
  11. 11.
    Maji, P., Pal, S.K.: Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans. Syst. Man Cybern. Part B: Cybernet. 37(6), 1529–1540 (2007)CrossRefGoogle Scholar
  12. 12.
    Krishnapuram, R., Keller, J.M.: The Possibilistic C-Means Algorithm: Insights and Recommendations. IEEE Trans. Fuzzy Syst. 4(3), 385–393 (1996)Google Scholar
  13. 13.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Berlin (1991)CrossRefGoogle Scholar
  14. 14.
    Lingras, P., West, C.: Interval set clustering of web users with rough K-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)CrossRefGoogle Scholar
  15. 15.
    Maji, P., Pal, S.K.: Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging. Wiley-IEEE Computer Society Press, New Jersey (2012)CrossRefGoogle Scholar
  16. 16.
    Maji, P., Pal, S.K.: RFCM: a hybrid clustering algorithm using rough and fuzzy sets. Fundamenta Informaticae 80(4), 475–496 (2007)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Atanassov, K.: Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Zadeh, L.A.: Fuzzy sets. Inf. Comput. 8, 338–353 (1965)zbMATHGoogle Scholar
  19. 19.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall PTR, Upper Saddle River, NJ (1995)zbMATHGoogle Scholar
  20. 20.
    Montes, I., Pal, N.R., Janis, V., Montes, S.: Divergence measures for intuitionistic fuzzy sets. IEEE Trans. Fuzzy Syst. 23(2), 444–456 (2014)CrossRefGoogle Scholar
  21. 21.
    Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd ed. Wiley, Hoboken (2002) (Computing Surveys 31(3), 264–323 (1999))Google Scholar
  22. 22.
  23. 23.
    Melin, P., Castillo, O.: A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Applied Soft Com. 21, 568–577 (2014)CrossRefGoogle Scholar
  24. 24.
    Ghosh, S.K., Ghosh, A., Chakrabarti, A.: VEA: vessel extraction algorithm by active contour model and a novel wavelet analyzer for diabetic retinopathy detection. Int. J. Image Graphics 18(2) (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSister Nivedita UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringNetaji Subhash Engineering CollegeKolkataIndia

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