微晶片在SVM與SOM上的分析

A Study on Analyzing Microarray Data using SVM and SOM

王琮郁(Tsong-Yuh Wang)

指導教授 ： 陳朝欽

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2007年)

https://doi.org/10.6843/NTHU.2007.00129

摘要

基因的發現以及基因序列的完成促進微晶片快速的發展。研究人員認為修復基因將有助於治療疾病，在微晶片被建構出來後，從微晶片掃瞄圖中可算出基因表現量，我們的實驗主要在分析這些數字型態的基因表現量，藉由實驗結果來讓我們對基因的特性有更進一步的了解。
微晶片的成本相當昂貴，從有限的微晶片來求得重要的資訊是很重要的。我們先取得六組不同來源的微晶片基因表現量，接著由基因選取法選擇重要基因，然後我們將支援向量機與自我組織圖應用在這六組資料上作分類，藉由探討分析結果來探討基因選取法的效果以及基因的特性，而基因選取法所選取的少量基因，將有助於生物或醫學領域的研究。

關鍵字

微晶片 ； 支援向量機 ； 自我組織圖 ； 基因表現量 ； 基因選取

並列摘要

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.

並列關鍵字

microarray ； Support Sector Machines ； Self-Organizing Map ； gene expression ； gene selection

參考文獻

[Ali00] A.A. Alizadeh et al., "Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling," Nature, vol. 403, 503-511, February 2000.

[Alo99] U. Alon et al., "Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays," Proceedings of National Academy of Sciences of the United States of American, vol. 96, 6745-6750, 1999.

[Arm02] S.A. Armstrong et al., "MLL Translocations Specify A Distinct Gene Expression Profile that Distinguishes A Unique Leukemia," Nature Genetics, vol. 30, 41-47, January 2002.

[Chu03] H.Y. Chuang, H.K. Tsai, Y.F. Tsai and C.Y. Kao, “Ranking Genes for Discriminability on Microarray Data,” Journal of Information Science and Engineering, vol. 19, 953-966, 20 03.

[Cle88] W.S. Cleveland and S.J. Devlin, "Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting," Journal of the American Statistical Association, vol. 83, 596-610, 1988.