Gene Expression Pattern Extraction Based on Wavelet Analysis
By viewing a gene expression profile as a pseudtime signal, we apply wavelet transformation (WT) to analyze gene expression data in a time-frequency manner. As a result, two pattern extraction approaches, continuous wavelet transformation (CWT)-based one and discrete wavelet transformation (DWT)-based one, are proposed to extract hidden expression patterns for cancer classification and are compared. Gene expression data are highly redundant and highly noisy, and hidden gene correlation patterns play more important roles to cancer classification than any single gene or simple combinations of genes. The CWT can more efficiently detect the consistent correlation signature than the DWT due to the availability of more detail information. Testing results on two publicly available gene expression datasets show the effectiveness and efficiency of the CWT-based approach.
Xin-Ping Xie Xuan-Hao Ding
School of Mathematics and Computational Science,Guilin University of Electronic Technology,GuiLin,China
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1274-1278
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）