Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data

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Jayakishan Meher 1,* Ram Chandra Barik 1 Madhab Ranjan Panigrahi 2 Saroj Kumar Pradhan 3 Gananath Dash 4

1. Computer Science and Engg, Vikash College of Engineering for Women, Bargarh, Odisha, India

2. Chemical Engineering, Vikash College of Engineering for Women, Bargarh, Odisha, India

3. Electrical Engg, Veer Surendra Sai University of Technology, Burla, Odisha, India

4. School of Physics, Sambalpur University, Burla, Odisha, India

* Corresponding author.


Received: 2 Nov. 2011 / Revised: 4 Feb. 2012 / Accepted: 16 Apr. 2012 / Published: 8 Aug. 2012

Index Terms

Factor analysis, wavelet transform, gene expression data, radial basis function neural network


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


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