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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: Normalization Benefits Microarray-Based Classification | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2006 Article ID 43056 Pages 1-13 DOI 10.1155 BSB 2006 43056 Normalization Benefits Microarray-Based Classification Jianping Hua 1 Yoganand Balagurunathan 1 Yidong Chen 2 James Lowey 1 Michael L. Bittner 1 Zixiang Xiong 3 Edward Suh 1 and Edward R. Dougherty1 3 1 Computational Biology Division Translational Genomics Research Institute Phoenix AZ 85004 USA 2 Genetics Branch Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892-2152 USA 3 Department of Electrical Computer Engineering Texas A M University College Station TX 77843 USA Received 11 December 2005 Revised 19 April 2006 Accepted 18 May 2006 Recommended for Publication by Paola Sebastiani When using cDNA microarrays normalization to correct labeling bias is a common preliminary step before further data analysis is applied its objective being to reduce the variation between arrays. To date assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based phenotype classification it is important to evaluate microarray normalization procedures relative to classification. Using a modelbased approach we model the systemic-error process to generate synthetic gene-expression values with known ground truth. These synthetic expression values are subjected to typical normalization methods and passed through a set of classification rules the objective being to carry out a systematic study of the effect of normalization on classification. Three normalization methods are considered offset linear regression and Lowess regression. Seven classification rules are considered 3-nearest neighbor linear support vector machine linear discriminant analysis regular histogram Gaussian kernel perceptron and multiple perceptron with majority voting. The results of the first three
