Skip to main content

Advertisement

SpringerLink
Log in

A Novel Human Diabetes Biomarker Recognition Approach Using Fuzzy Rough Multigranulation Nearest Neighbour Classifier Model

  • Original research article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

The selection of gene identifier from microarray databases is a challenging task since microarray contains large number of gene attributes for a few samples. This article proposes a novel fuzzy-rough set-based gene expression features selection using fuzzy-rough reduct under multi-granular space for human diabetes patient. Firstly, fuzzy multi-granular gain has been computed from the expression datasets via fuzzy entropy which reduces the dimension of the database. Thereafter, the features have been selected from microarray using the fuzzy rough reduct and information gain with respect to their expression patterns. To reduce the computational cost, a decision making scheme has been designed using a rough approximation of a fuzzy concept in the field of multi-granulation framework. Finally, we have recognized the association among the genomes that have expressively different expression patterns from controlled state to the diabetic state with respect to their impression using modified fuzzy-rough nearest neighbour classifier (FRNNC). Five standard diabetic microarray datasets have been considered to quantify the efficiency of the designed FRNNC model and are validated with F measure using diabetes gene expression NCBI database and it performs superior compared to existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Thomas A, Rebecca L (2015) Prevalence of diabetic retinopathy within a national diabetic retinopathy screening service. Br J Ophthalmol 99(1):64–68. https://doi.org/10.1136/bjophthalmol-2013-304017

    Article  PubMed  Google Scholar 

  2. Florez JC, Manning AK, Dupuis J (2007) A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets. Diabetes 56(12):3063–3074. https://doi.org/10.2337/db07-0451

    Article  CAS  PubMed  Google Scholar 

  3. Hanson RL, Bogardus C, Duggan D (2007) A search for variants associated with young-onset type 2 diabetes in Americal Indians in 100K genotyping array. Diabetes 56(12):3045–3052. https://doi.org/10.2337/db07-0462

    Article  CAS  PubMed  Google Scholar 

  4. Rmapersaud E, Damcott CM, Fu M (2007) Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the old order amish: evidence for replication from diabetes related quantitative traits and from independent populations. Diabetes 56(12):3053–3062. https://doi.org/10.2337/db07-0457

    Article  CAS  Google Scholar 

  5. Das R, Kalita J, Bhattacharyya DK (2011) A pattern matching approach for clustering gene expression data. Int J Data Min Model Manag. https://doi.org/10.1504/IJDMMM.2011.041492

    Article  Google Scholar 

  6. Jiang D, Peri J, Zhang A (2003) DHC: a density based hierarchical clustering methods for time series gene expression data. IEEE Int Symp Bioinform Bioeng. https://doi.org/10.1109/BIBE.2003.1188978

    Article  Google Scholar 

  7. Dudoit S, Fridlyand J, Speed T (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97(457):77–87. https://doi.org/10.1198/016214502753479248

    Article  CAS  Google Scholar 

  8. Nayak RK, Mishra D, Shaw K, Mishra S (2012) Rough set based attribute clustering for sample classification of gene expression data. Int Conf Model Optim Comput. https://doi.org/10.1016/j.proeng.2012.06.219

    Article  Google Scholar 

  9. Banerjee M, Mitra S, Banka H (2007) Evolutionary Rough feature selection in gene expression data. IEEE Trans Syst Man Cybern Part C Appl Rev. https://doi.org/10.1109/TSMCC.2007.897498

    Article  Google Scholar 

  10. Maji P, Pal SK (2007) Protein sequence analysis using relational soft clustering algorithms. Int J Comput Math 84(5):599–617. https://doi.org/10.1080/00207160701210083

    Article  Google Scholar 

  11. Tong MKH, Liu C, Xu W (2013) An ensemble of SVM classifiers based on gene pairs. Comput Biol Med 43(6):729–737. https://doi.org/10.1016/j.compbiomed.2013.03.010

    Article  CAS  PubMed  Google Scholar 

  12. Danaee P, Hendrix DA (2017) A deep learning approach for cancer detection and relevant gene identification. Pac Symp Biocomput. https://doi.org/10.1142/9789813207813_0022

    Article  PubMed  Google Scholar 

  13. Xie R, Quitadamo A, Cheng J, Shi X (2016) A predictive model of gene expression using a deep learning framework. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM). https://doi.org/10.1109/BIBM.2016.7822599

  14. Jia L, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl 59:13–19. https://doi.org/10.1016/j.eswa.2016.04.020

    Article  Google Scholar 

  15. Gao L, Ye M et al (2017) Hybrid method based on information gain and support vector machine for gene selection in cancer classification. Genom Proteom Bioinform 15:389–395. https://doi.org/10.1016/j.gpb.2017.08.002

    Article  Google Scholar 

  16. Lu H, Chen J et al (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56–62. https://doi.org/10.1016/j.neucom.2016.07.0800

    Article  Google Scholar 

  17. Sarah MA, Saleh AI, Labib M (2019) Gene expression cancer classification using modified K-nearest neighbors technique. Biosystems 176:41–51. https://doi.org/10.1016/j.biosystems.2018.12.009

    Article  CAS  Google Scholar 

  18. Abualigah L, Shehab M, Alshinwan M et al (2020) Ant lion optimizer: a comprehensive survey of its variants and applications. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09420-6

    Article  Google Scholar 

  19. Abualigah L, Diabat A, Geem ZW (2020) A comprehensive survey of the harmony search algorithm in clustering applications. Appl Sci 10(11):3827. https://doi.org/10.3390/app10113827

    Article  CAS  Google Scholar 

  20. Sun L, Kong X, Xu J et al (2019) A hybrid gene selection method based on relieff and ant colony optimization algorithm for tumor classification. Sci Rep 9:8978. https://doi.org/10.1038/s41598-019-45223-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zadeh LA (1999) Fuzzy logic = computing with words. In: Zadeh LA, Kacprzyk J (eds) Computing with words in information/intelligent systems 1. Studies in fuzziness and soft computing, vol 33. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1873-4_1

    Chapter  Google Scholar 

  22. Polkowski L, Skowron A (1998) Rough sets in knowledge discovery. Studies in fuzziness and soft computing series. Physica-Verlag, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3

    Book  Google Scholar 

  23. Qu Y, Shen Q, Mac-Parthalain N, Shang C, Wu W (2012) Fuzzy similarity-based nearest-neighbour classification as alternatives to their fuzzy-rough parallels. Int J Approx Reason 54(1):184–195. https://doi.org/10.1016/j.ijar.2012.06.008

    Article  Google Scholar 

  24. Ghosh A, De RK (2016) Fuzzy correlation association mining: selection altered associations among the genes, and some possible marker genes mediating certain cancers. Appl Soft Comput 38:587–605. https://doi.org/10.1016/j.asoc.2015.09.057

    Article  Google Scholar 

  25. Nguyen T, Nahavandi S (2016) Modified AHP for gene selection and cancer classification using type-2 fuzzy logic. IEEE Trans Fuzzy Syst 24(2):273–287. https://doi.org/10.1109/TFUZZ.2015.2453153

    Article  Google Scholar 

  26. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Norwell. https://doi.org/10.1007/978-94-011-3534-4

    Book  Google Scholar 

  27. Hu Q, Zhang L, An S, Zhang D, Yu D (2012) On robust fuzzy rough set models. IEEE Trans Fuzzy Syst 20(4):636–651. https://doi.org/10.1109/TFUZZ.2011.2181180

    Article  Google Scholar 

  28. Sun B, Ma W, Qian Y (2017) Multigranulation fuzzy rough set over two universes and its application to decision making. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2017.01.036

    Article  Google Scholar 

  29. Jensen R, Parthalain NM (2015) Towards scalable fuzzy—rough feature selection. Inf Sci 15:1–15. https://doi.org/10.1016/j.ins.2015.06.025

    Article  CAS  Google Scholar 

  30. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall PTR, Upper Saddle River. https://doi.org/10.1021/ci950144a

    Book  Google Scholar 

  31. Li J, Zhang L, Li H et al (2019) Integrated entropy-based approach for analyzing exons and introns in DNA sequences. BMC Bioinform 20:283. https://doi.org/10.1186/s12859-019-2772-y

    Article  CAS  Google Scholar 

  32. Sarkar M (2007) Fuzzy-rough nearest neighbors algorithm. Fuzzy Sets Syst 158:2123–2152. https://doi.org/10.1016/j.tcs.2011.05.040

    Article  Google Scholar 

  33. Jensen R, Cornelis C (2011) Fuzzy-rough nearest neighbour classification and prediction. Theor Comput Sci 412:5871–5884. https://doi.org/10.1016/j.tcs.2011.05.040

    Article  Google Scholar 

  34. Available: http://www.ncbi.nlm.nih.gov/geo

  35. Liu H, Li J, Wong L (2002) A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Gene Inform 13:51–60. https://doi.org/10.1016/j.procs.2013.10.003

    Article  CAS  Google Scholar 

  36. Melin P, Castillo OA (2014) Review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl Soft Comput 21:568–577. https://doi.org/10.1016/j.asoc.2014.04.017

    Article  Google Scholar 

  37. Ghosh SK, Ghosh A, Chakrabarti A (2018) VEA: vessel extraction algorithm by active contour model and a novel wavelet analyzer for diabetic retinopathy detection. Int J Image Gr. https://doi.org/10.1142/S0219467818500080

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India

    Swarup Kr Ghosh

  2. Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India

    Anupam Ghosh

Authors
  1. Swarup Kr Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anupam Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swarup Kr Ghosh.

Ethics declarations

Conflict of interest

There is no conflict of interest in this research.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, S., Ghosh, A. A Novel Human Diabetes Biomarker Recognition Approach Using Fuzzy Rough Multigranulation Nearest Neighbour Classifier Model. Interdiscip Sci Comput Life Sci 12, 461–475 (2020). https://doi.org/10.1007/s12539-020-00391-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-020-00391-7

Keywords

Search

Navigation