An Efficient Artificial Bee Colony and Fuzzy C Means Based Co-regulated Biclustering from Gene Expression Data

  • K. Sathishkumar
  • E. Balamurugan
  • P. Narendran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


The gene microarray data are arranged based on the pattern of gene expression using various clustering algorithms and the dynamic natures of biological processes are generally unnoticed by the traditional clustering algorithms. To overcome the problems in gene expression analysis, novel algorithms for finding the coregulated clusters, dimensionality reduction and clustering have been proposed. The coregulated clusters are determined using biclustering algorithm, so it is called as coregulated biclusters. The coregulated biclusters are two or more genes which contain similarity features. The dimensionality reduction of microarray gene expression data is carried out using Locality Sensitive Discriminant Analysis (LSDA). To maintain bond between the neighborhoods in locality, LSDA is used and an efficient meta heuristic optimization algorithm called Artificial Bee Colony (ABC) using Fuzzy C Means clustering is used for clustering the gene expression based on the pattern. The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes lesser less clustering time when compared with existing algorithms.


Gene expression data Bimax Algorithm Co-regulated Biclusters Locality Sensitive Discriminant Analysis Artificial Bee Colony Fuzzy C Means 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Belcastro, V., Gregoretti, F., Siciliano, V., Santoro, M., D’Angelo, G., Oliva, G., di Bernardo, D.: Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(3), 668–678 (2012)CrossRefGoogle Scholar
  2. 2.
    Yuan, Y., Li, C.-T.: Partial Mixture Model for Tight Clustering in Exploratory Gene Expression Analysis. In: Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007 (2007)Google Scholar
  3. 3.
    Yin, L., Huang, C.-H.: Clustering of Gene Expression Data: Performance and Similarity Analysis. In: First International Multi-Symposiums on Computer and Computational Sciences, IMSCCS 2006 (2006)Google Scholar
  4. 4.
    Jiang, D., Pei, J., Zhang, A.: “DHC: a density-based hierarchical clustering method for time series gene expression data’. In: Proceedings of the 3rd IEEE International Symposium on Bioinformatics and Bioengineering, Bethesda, Maryland, USA, p. 393 (2003)Google Scholar
  5. 5.
    Dhiraj, K., Rath, S.K., Pandey, A.: Gene Expression Analysis Using Clustering. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2009 (2009)Google Scholar
  6. 6.
    Yano, N., Kotani, M.: Clustering gene expression data using self-organizing maps and k-means clustering. In: SICE 2003 Annual Conference, vol. 3, pp. 3211–3215 (2003)Google Scholar
  7. 7.
    Chung, S., Jun, J., McLeod, D.: Mining geneexpression datasets using density based clustering. Technical Report, USC/IMSC, University of Southern California, No. IMSC-04-002 (2004)Google Scholar
  8. 8.
    Syamala, R., Abidin, T., Perrizo, W.: Clustering Microarray Data based on Density and Shared Nearest Neighbor Measure. In: Proceedings of the 21st ISCA International Conference on Computers and Their Applications (CATA 2006), pp. 23–25 (2006)Google Scholar
  9. 9.
    Fu, L., Medico, E.: FLAME: A novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics 8(3) (2007)Google Scholar
  10. 10.
    Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality Sensitive Discriminant Analysis (2007)Google Scholar
  11. 11.
    Geng, X., Tao, F.: GNRFCM: A new fuzzy clustering algorithm and its application. In: International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII (2012)Google Scholar
  12. 12.
    Wen, J.: Ontology Based Clustering for Improving Genomic IR. Twentieth IEEE International Symposium International Journal of Data Mining and Bioinformatics 3(3), 229–259 (2009)Google Scholar
  13. 13.
    Chandran, C.P., IswaryaLakshmi, K.: Biclustering analysis of coregulatedbiclusters from gene expression data. International Journal of Computational Intelligence and Informatics 2(1) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • K. Sathishkumar
    • 1
  • E. Balamurugan
    • 2
  • P. Narendran
    • 1
  1. 1.Gobi Arts & Science CollegeGobichettipalayamIndia
  2. 2.Bannari Amman institute of TechnologySathyamangalamIndia

Personalised recommendations