Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Detect Key Gene Information in Classification of Microarray Data | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 612397 10 pages doi 2008 612397 Research Article Detect Key Gene Information in Classification of Microarray Data Yihui Liu School of Computer Science and Information Technology Shandong Institute of Light Industry Jinan Shandong 250353 China Correspondence should be addressed to Yihui Liu yxl@ Received 10 November 2007 Revised 1 March 2008 Accepted 14 April 2008 Recommended by . Chung We detect key information ofhigh-dimensional microarray profiles based on wavelet analysis and genetic algorithm. Firstly wavelet transform is employed to extract approximation coefficients at 2nd level which remove noise and reduce dimensionality. Genetic algorithm GA is performed to select the optimized features. Experiments are performed on four datasets and experimental results prove that approximation coefficients are efficient way to characterize the microarray data. Furthermore in order to detect the key genes in the classification of cancer tissue we reconstruct the approximation part of gene profiles based on orthogonal approximation coefficients. The significant genes are selected based on reconstructed approximation information using genetic algorithm. Experiments prove that good performance of classification is achieved based on the selected key genes. Copyright 2008 Yihui Liu. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Recently hugeadvances in DNA microarrayhave allowed the scientist to test thousands of genes in normal or tumor tissues on a single array and check whether those genes are active hyperactive or silent. Therefore there is an increasing interest in changing the criterion of tumor classification from morphologic to molecular. In this perspective the .
