Analysis of Microarray Data by Empirical Wavelet Transform for Cancer Classification Using Block by Block Method

In this study, DNA microarray data is analyzed from a signal processing perspective for cancer classification. An adaptive wavelet transform named Empirical Wavelet Transform (EWT) is analyzed using block-by-block procedure to characterize microarray data. The EWT wavelet basis depends on the input data rather predetermined like in conventional wavelets. Thus, EWT gives more sparse representations than wavelets. The characterization of microarray data is made by block-by-block procedure with predefined block sizes in powers of 2 that starts from 128 to 2048. After characterization, a statistical hypothesis test is employed to select the informative EWT coefficients. Only the selected coefficients are used for Microarray Data Classification (MDC) by the Support Vector Machine (SVM). Computational experiments are employed on five microarray datasets; colon, breast, leukemia, CNS and ovarian to test the developed cancer classification system. The obtained results demonstrate that EWT coefficients with SVM emerged as an effective approach with no misclassification for MDC system.