CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2009 vol.6 Issue No.02 - April-June

Subscribe

Issue No.02 - April-June (2009 vol.6)

pp: 244-259

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.15

ABSTRACT

This paper presents Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) of microarray data for finding differentially expressed genes (DEGs) from two-sample microarray experiments. The concept of fuzzy membership is introduced to transform the hard adaptive subspace iteration (ASI) algorithm into a fuzzy-ASI algorithm to perform two-way clustering. The proposed approach follows a progressive framework to assign a relevance value to genes associated with each cluster. Subsequently, each gene cluster is scored and ranked based on its potential to provide a correct classification of the sample classes. These ranks are converted into P values using the R-test, and the significance of each gene is determined. A fivefold validation is performed on the DEGs selected using the proposed approach. Empirical analyses on a number of simulated microarray data sets are conducted to quantify the results obtained using the proposed approach. To exemplify the efficacy of the proposed approach, further analyses on different real microarray data sets are also performed.

INDEX TERMS

Clustering, classification and association rules, data mining, data and knowledge visualization, feature extraction or construction.

CITATION

Jahangheer Shaik, Mohammed Yeasin, "Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray Data",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.6, no. 2, pp. 244-259, April-June 2009, doi:10.1109/TCBB.2008.15REFERENCES

- [5] D.L. Davies and D.W. Bouldin, “A Cluster Separation Measure,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 1, pp.224-227, 1979.- [10] S. Tavazoie, J. Hughes, M. Campbell, R. Cho, and G. Church, “Cluster Analysis and Display of Genome Wide Expression Patterns,”
Nature Genetics, vol. 22, pp. 281-285, 1999.- [13] P.J. Woolf and Y. Wang, “A Fuzzy Logic Approach to Analyzing Gene Expression Data,”
Physiology of Genomics, vol. 2, pp. 9-15, 2000.- [14] N. Belacel, M. Cuperlovic, R. Ouellette, and M.R. Boulassel, “The Variable Neighborhood Search Metaheuristic for Fuzzy Clustering cDNA Microarray Gene Expression Data,”
Artificial Intelligence and Applications, vol. 411, 2004.- [15] M. Ceccarelli and A. Maratea, “Semi-Supervised Fuzzy C-Means Clustering of Biological Data,”
Proc. Sixth Int'l Workshop Fuzzy Logic (WILF '05), pp. 259-266, 2005.- [16] L.M. Fu and E. Medico, “FMC, A Fuzzy Map Clustering Algorithm for Microarray Data Analysis,”
Proc. Bioinformatics Italian Soc. Meeting (BITS), 2004.- [17] S.Y. Kim, T.M. Choi, and J.S. Bae, “Fuzzy Types Clustering for Microarray Data,”
Int'l J. Computational Intelligence, vol. 2, pp. 12-15, 2005.- [20] J. Shaik and M. Yeasin, “A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes,”
Proc. Int'l Joint Conf. Neural Networks (IJCNN '06), pp. 5287-5292, 2006.- [23] K.S. Pollard and M.J.v.d. Laan, “Statistical Inference for Simultaneous Clustering of Gene Expression Data,”
Math. Biosciences, vol. 176, pp. 9121-9126, 2002.- [24] Y. Benjamini and Y. Hochberg, “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,”
J.Royal Statistical Soc., vol. 57, pp. 289-300, 1995.- [26] I. Lonnstedt and T. Speed, “Replicated Microarray Data,”
Statistica Sinica, vol. 12, pp. 31-46, 2002.- [27] S. Mukherjee, S.J. Roberts, and M.J. Laan, “Data-Adaptive Test Statistics for Microarray Data,”
Bioinformatics, vol. 21, pp. 108-114, 2005.- [29] J. Shaik and M. Yeasin, “Two-Way Clustering Using Fuzzy-ASI for Knowledge Discovery in Microarrays,”
Proc. IEEE Symp. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2007.- [30] B. Zhang, D. Schmoyer, S. Kirov, and J. Snoddy, “GOTree Machine (GOTM): A Web Based Platform for Interpreting Sets of Interesting Genes Using Gene Ontology Hierarchies,”
BMC Bioinformatics, vol. 5, pp. 1-8, 2004.- [31] Y. Su, T.M. Murali, V. Pavlovic, M. Schaffer, and S. Kasif, “Rankgene: Identification of Diagnostic Genes Based on Expression Data,” http://genomics10.bu.edu/yangsurankgene/, 2002.
- [32] K. Fujarewicz and M. Wiench, “Selecting Differentially Expressed Genes for Colon Tumor Classification,”
Int'l J. Applied Math. and Computer Science, vol. 13, pp. 327-335, 2003. |