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Comparative Analysis and Evaluation of Biclustering Algorithms for Microarray Data

  • Ankush MaindEmail author
  • Shital Raut
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 4)

Abstract

From the last decade, the concept of biclustering becomes very popular for the analysis of gene expression data. This is because of the advantages of biclustering algorithms over the drawbacks of clustering algorithms on gene expression data. Many biclustering algorithms have been published in recent years. Some of them performed well on gene expression data and other have some issues. In this paper, analysis of some popular biclustering algorithms have been done with the help of experimental study. Along with this, survey of all the bicluster quality measures which have been used for extracting biologically significant biclusters in various biclustering algorithms is also given. For the experimental study, synthetic dataset has been used. Based on the experimental study, some comparative analyses have been done, and some important issues related to the biclustering algorithms have been pointed out. From this analytical as well as experimental study, newcomers who are interested to do the research in the area of biclustering will get proper direction for the better research.

Keywords

Biclustering Gene expression data Biologically significant etc. 

References

  1. 1.
    Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, FABIA: factor analysis for bicluster acquisition. Bioinformatics, Vol. 26. (2010) 1520–1527.Google Scholar
  2. 2.
    T.M. Murali, S. Kasif, Extracting conserved gene expression motifs from gene expression data, Pacific Symposium on Biocomputing, (2003) 77–88.Google Scholar
  3. 3.
    Madeira, S.C. and Oliveira, A.L. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. Vol. 1. (2004) 24–45.Google Scholar
  4. 4.
    J. Hartigan, Direct clustering of a data matrix, J. Am. Stat. Assoc. Vol. 67. (1972) 123–129.Google Scholar
  5. 5.
    Cheng, Y. and Church, G. Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. (2000) 93–103.Google Scholar
  6. 6.
    L. Lazzeroni, A. Owen, Plaid models for gene expression data, Stat. Sinica. Vol. 12. (2002) 61–86.Google Scholar
  7. 7.
    Y. Kluger, R. Basri, J. Chang, M. Gerstein, Spectral bicluster of microarray data: coclustering genes and conditions, Genome Res. Vol. 13. (2003) 703–716.Google Scholar
  8. 8.
    J. Yang, H. Wang, W. Wang, P.S. Yu., An improved biclustering method for analyzing gene expression profiles, Int. J. Artif. Intell. Tools. Vol. 14. (2005) 771–790.Google Scholar
  9. 9.
    A. Tanay, R. Sharan, R. Shamir, Discovering statistically significant biclusters in gene expression data, Bioinformatics, Vol. 18. (2002) 136–144.Google Scholar
  10. 10.
    H. Ahmed, P. Mahanta, D. Bhattacharyya, J. Kalita, Shifting-and-scaling correlation based biclustering algorithm, IEEE/ACM Trans. Comput. Biol. Bioinform. Vol. 11. (2014) 1239–1252.Google Scholar
  11. 11.
    S. Roy, D.K. Bhattacharyya, J.K. Kalita, CoBi: pattern based co-regulated biclustering of gene expression data, Pattern Recogn. Lett., Vol. 34. (2013) 1669–1678.Google Scholar
  12. 12.
    T. Yun, G.-S. Yi, Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion, BMC Genom., Vol. 14. (2013) 144.Google Scholar
  13. 13.
    P. Baldi and G.W. Hatfield, DNA Microarrays and Gene Expression. From Experiments to Data Analysis and Modelling. Cambridge Univ. Press, 2002.Google Scholar
  14. 14.
    S. Bergmann, J. Ihmels, N. Barkai, Iterative signature algorithm for the analysis of large-scale gene expression data, Phys. Rev., Vol. 67. (2003) 031902.Google Scholar
  15. 15.
    Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, et al., A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics. Vol. 22. (2006) 1122–1129.Google Scholar
  16. 16.
    A. Ben-Dor, B. Chor, R.M. Karp, Z. Yakhini. 2003. Discovering local structure in gene expression data: the order-preserving submatrix problem. J. Comput. Biol. 10, 3–4 (2003), 373–384.Google Scholar
  17. 17.
    A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, A novel coherence measure for discovering scaling biclusters from gene expression data, J. Bioinform. Comput. Biol. Vol. 7. (2009) 853–868.Google Scholar
  18. 18.
    Yip K, Cheung D, Ng M, Harp: A practical projected clustering algorithm. IEEE Transactions on Knowledge and Data Engineering, Vol. 16. 1387–1397.Google Scholar
  19. 19.
    Li Teng and Laiwan Chan. Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. Signal Processing Systems. Vol. 50. 267–280.Google Scholar
  20. 20.
    Ayadi W, Elloumi M, Hao J, A biclustering algorithm based on a bicluster enumeration tree: application to dna microarray data. BioData mining, Vol. 2. (2009) 1–16.Google Scholar
  21. 21.
    F. Divina, B. Pontes, R. Giráldez, J.S. Aguilar-Ruiz, An effective measure for assessing the quality of biclusters, Comput. Biol. Med., Vol. 42. (2012) 245–256.Google Scholar
  22. 22.
    Pontes B, Giráldez R, Aguilar-Ruiz J Measuring the quality of shifting and scaling patterns in biclusters. Pattern Recognition in Bioinformatics, (2010) 242–252.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science & Engineering DepartmentVNITNagpurIndia

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