Mixed PCA and Wavelet Transform based Effective Feature Extraction for Efficient Tumor Classification using DNA Microarray Gene Expression Data

Abstract

Cancer classification is an emerging research area in the field of bioinformatics. Gene expression profiles using microarray data play important role in accurate tumor diagnosis. Hence correlation between gene expression profiles to disease through microarray data and its analysis has been an intensive task in molecular biology. As the microarray data have thousands of genes and very few samples, it is crucial to develop techniques to effectively exploit the huge quantity of data produced. Thus efficient feature extraction and computational method development is indispensible for the analysis. In this paper a mixed feature extraction method by combining principal component analysis (PCA) and discrete wavelet transform (DWT) has been proposed to detect informative genes effectively. The PCA is a dimensionality reduction algorithm which aims to map high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Further a feature extraction method based on the DWT is proposed. The approximation coefficients obtained by the decomposition at a particular level is used as the features for further study. 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 with low computational complexity and high accuracy.