Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(454.9KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Gene Expression Based Acute Leukemia Cancer Classification: A Neuro-Fuzzy Approach
B.B.M.Krishna Kanth, U.V.kulkarni, B.G.V.Giridhar
Pages - 136 - 146     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - September 2010  Table of Contents
MORE INFORMATION
KEYWORDS
gene expression data, cancer classification, membership function, AAL/AML
ABSTRACT
In this paper, we proposed the Modified Fuzzy Hypersphere Neural Network (MFHSNN) for the discrimination of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) in leukemia dataset. Dimensionality reduction methods, such as Spearman Correlation Coefficient and Wilcoxon Rank Sum Test are used for gene selection. The performance of the MFHSNN system is encouraging when benchmarked against those of Support vector machine (SVM) and the K-nearest neighbor (K-NN) classifiers. A classification accuracy of 100% has been achieved using the MFHSNN classifier using only two genes. Furthermore, MFHSNN is found to be much faster with respect to training and testing time.
CITED BY (3)  
1 Kaushik, A. (2014). M. Tech. Thesis (Doctoral dissertation, punjab technical university).
2 Alejandro Rosales-Pérez, Carlos A. Reyes-García, Pilar Gómez-Gil, Jesus A. Gonzalez , Leopoldo Altamirano (2011), “Genetic Selection of Fuzzy Model for Acute Leukemia Classification ”, Advances in Artificial Intelligence Lecture Notes in Computer Science, Vol. 7094/2011, pp. 537-548, 2011.
3 Rosales-Pérez, A., Reyes-García, C. A., Gómez-Gil, P., Gonzalez, J. A., & Altamirano, L. (2011). Genetic selection of fuzzy model for acute leukemia classification. In Advances in Artificial Intelligence (pp. 537-548). Springer Berlin Heidelberg.
1 Google Scholar 
2 Academic Journals Database 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 PDFCAST 
14 PdfSR 
1 M. Schena, D. Shalon, R. W. Davis and P. O. Brown. “Quantitative monitoring of gene expression patterns with a complementary DNA microarray”, Science 267 (1995):pp. 467–470.
2 T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield and E. S. Lander. “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring”, Science, vol. 286, pp. 531–537, 1999.
3 R. Baumgartner, C. Windischberger, and E. Moser. “Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis”. Magn Reson Imaging, vol. 16, no. 2, pp. 115–125, 1998.
4 T. Kohonen, Ed. “Self-organizing maps”. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1997.
5 T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler. “Support vector machine classification and validation of cancer tissue samples using microarray expression data”, Bioinformatics, vol. 16, pp. 906–914, 2000.
6 J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, and P. S. Meltzer. “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks”, Nature Medicine, vol. 7, pp. 673–679, 2001.
7 C. Shi and L. Chen. “Feature dimension reduction for microarray data analysis using locally linear embedding”, APBC, 2005, pp. 211–217.
8 L. Li, C. R. Weinberg, T. A. Darden, and L. G. Pedersen. “Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the ga/knn method”, Bioinformatics, vol. 17, pp. 1131–1142, 2001.
9 T. Jirapech-Umpai and S. Aitken. “Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes”, Bioinformatics, vol. 6, pp. 168–174, 2005.
10 Min Su, M. Basu and A. Toure. “Multi-Domain Gating Network for Classification of Cancer Cells Using Gene Expression Data”, In Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 286–289, 2002.
11 R Xu. G. Anagnostopoulos and D. Wunsch. ”Tissue Classification Through Analysis of Gene Expression Data Using A New Family of ART Architectures”, In Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 300–304, 2002.
12 Saeys Y, Inza I, Larranaga P. “A review of feature selection techniques in bioinformatics”, Bioinformatics 2007, 23(19): 2507-2517.
13 Wang X, Gotoh O. “Microarray-Based Cancer Prediction Using Soft Computing Approach”, Cancer Informatics, 2009, 123–39.
14 U V Kulkarni, T R Sontakke. “Fuzzy Hypersphere Neural Network Classifier”, 10th IEEE int. conference on fuzzy systems, Dec 2001, 1559-1562.
15 S.-B. Cho, J. Ryu. “Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features”, Proc. IEEE 90 (11) (2002):1744–1753.
16 E.L. Lehmann. “Non-parametrics: Statistical Methods Based on Ranks”. Holden-Day, San Francisco, 1975.
17 Deng Lin1, MAJinwen1 & PEI Jian2. “Rank sum method for related gene selection and its application to tumor diagnosis”, Chinese Science Bulletin 2004. Vol. 49, No. 15, 1652-1657.
18 Devore, J. L. “Probability and Statistics for Engineering and the Sciences”. 4th edition. California, Duxbury Press (1995).
Mr. B.B.M.Krishna Kanth
S.R.T.M.University - India
bbkkanth@yahoo.com
Dr. U.V.kulkarni
S.R.T.M.University - India
Dr. B.G.V.Giridhar
Andhra Medical College - India