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 sha** 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rebecca, T.L.: Prevalence of diabetic retinopathy within a national diabetic retinopathy screening service. British J. Ophthal. 99(1), 64–68 (2015)
Hanson, R.L., Bogardus, C., Duggan, D.: A search for variants associated with young-onset type 2 diabetes in Americal Indians in 100K genoty** array. Diabetes 56(12), 3045–3052 (2007)
Stekel, D.: Microarray Bioinformatics. Cambridge University Press, Cambridge (2006)
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)
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)
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)
Kalaiselvi, N., Inbarani, H.: A fuzzy soft set based classification for gene expression data. IJSER 3, 1315–1321 (2014)
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)
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
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. ISBN 0-306-40671-3 (1981)
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)
Krishnapuram, R., Keller, J.M.: The Possibilistic C-Means Algorithm: Insights and Recommendations. IEEE Trans. Fuzzy Syst. 4(3), 385–393 (1996)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Berlin (1991)
Lingras, P., West, C.: Interval set clustering of web users with rough K-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)
Maji, P., Pal, S.K.: Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging. Wiley-IEEE Computer Society Press, New Jersey (2012)
Maji, P., Pal, S.K.: RFCM: a hybrid clustering algorithm using rough and fuzzy sets. Fundamenta Informaticae 80(4), 475–496 (2007)
Atanassov, K.: Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg (1999)
Zadeh, L.A.: Fuzzy sets. Inf. Comput. 8, 338–353 (1965)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall PTR, Upper Saddle River, NJ (1995)
Montes, I., Pal, N.R., Janis, V., Montes, S.: Divergence measures for intuitionistic fuzzy sets. IEEE Trans. Fuzzy Syst. 23(2), 444–456 (2014)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd ed. Wiley, Hoboken (2002) (Computing Surveys 31(3), 264–323 (1999))
Available http://www.ncbi.nlm.nih.gov/geo
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghosh, S.K., Ghosh, A. (2020). A Novel Clustering-Based Gene Expression Pattern Analysis for Human Diabetes Patients Using Intuitionistic Fuzzy Set and Multigranulation Rough Set Model. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_88
Download citation
DOI: https://doi.org/10.1007/978-981-15-4032-5_88
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4031-8
Online ISBN: 978-981-15-4032-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)