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A Study on Analyzing Microarray Data using SVM and SOM
Thesis

A Study on Analyzing Microarray Data using SVM and SOM

Tsong-Yuh Wang
Masters, 國立清華大學, 資訊系統與應用研究所
2006

Abstract

微晶片 支援向量機 自我組織圖 基因表現量 基因選取 microarray Support Sector Machines Self-Organizing Map gene expression gene selection
Researchers majoring in biology and medical science believe we could cure kinds of diseases if the genes which lead to the diseases are fixed. The development of microaray grew fast in recent years in order to make human genes readable. When we get a microarray image, the gene expression values can be computed by segmentation methods. After the gene expression is computed from the microarray image, the following important research is to analyze the gene expression. Because the range of the value of the gene expression is too huge for us to compute, we have to normalize the gene expression values first. Then we use smoothly clipped absolute deviation (SCAD) SVM and weighted punishment on overlap (WEPO) to screen the important genes. When these important genes (features) are found out, they are used in two classification methods, support vector machines (SVM) and self-organizing map (SOM). Finally, we can understand more properties of microarray data by the experimental results of gene selection and classification methods.

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