International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 4, No. 9, 8 Aug. 2012
Cover page and Table of Contents: PDF (size: 1043KB)
Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data
Full Text (PDF, 1043KB), PP.73-79
Views: 0 Downloads: 0
Author(s)
Jayakishan Meher
1,*
Ram Chandra Barik
1
Madhab Ranjan Panigrahi
2
Saroj Kumar Pradhan
3
Gananath Dash
4
Index Terms
Factor analysis, wavelet transform, gene expression data, radial basis function neural network
Abstract
Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA) with discrete wavelet transform (DWT) to detect informative genes. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.
Cite This Paper
Jayakishan Meher, Ram Chandra Barik, Madhab Ranjan Panigrahi, Saroj Kumar Pradhan, Gananath Dash, "Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.9, pp.73-79, 2012. DOI:10.5815/ijitcs.2012.09.10
Reference
[1]Xiong M., Jin L., Li W. and Boerwinkle E. Computational methods for gene expression-based tumor classification. BioTechniques, 2000, vol. 29, no. 6, pp. 1264–1268.
[2]Baldi P. and Long A.D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 2001, vol. 17, no. 6, pp. 509–519.
[3]Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., Lander, E. S. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring Science, 1999, 286(5439), pp.531-537.
[4]Huang D.S. and Zheng C. H. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data. Bioinformatics, 2006, vol. 22, no. 15, pp. 1855–1862.
[5]Yeung K.Y., Ruzzo W. L. Principal component analysis for clustering gene expression data. Bioinformatics, 2002, 17, pp.763–774.
[6]Yihui Liu. Wavelet feature extraction for high-dimensional microarray data. Neurocomputing, 2009, Vol. 72, pp. 985-990.
[7]Yihui Liu. Detect Key Gene Information in Classification of Microarray Data. EURASIP Journal on Advances in Signal Processing, 2007 pp.1-10.
[8]Tan AC, Gilbert D. Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics, 2003, 2, pp.75-83.
[9]Dettling M. Bag Boosting for tumor classification with gene expression data. Bioinformatics, 2004 vol. 20, no. 18, pp. 3583–3593.
[10]Guyon I, Weston J, Barnhill and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn, 2002, 46, pp. 389- 422.
[11]Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., Meltzer, P. S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 2001, 7(6), pp.673-679.
[12]O'Neill MC and Song L. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect. BMC Bioinformatics, 2003, 4:13.
[13]Liu Bing, Cui Qinghua, Jiang Tianzi and Ma. Songde. A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinformatics, 2004. 5:136, pp. 1-12.
[14]Grossmann A. and Morlet J. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis, 1984, vol. 15, no. 4, pp.723–736.
[15]Mallat S. G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, vol. 11, no. 7, pp. 674–693.