Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/17864
標題: 核心邏輯斯迴歸模式之微陣列資料分析:次序類別的癌症分類
Kernel logistic regression based microarray data analysis: The ordinal scale cancer classification
作者: 陳陵姿
Chen, Ling-Zi
關鍵字: microarray;微陣列;classification;kernel method;logistic regression;ordered categories;分類;核心方法;邏輯斯迴歸;次序類別
出版社: 應用數學系所
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摘要: 
微陣列技術已被廣泛應用在癌症研究上,藉由分析資料列陣中的基因表現,進階找出癌症的分子變異區別。在本文裡,藉由微陣列資料分析於多級癌症分類的技巧,對比現況多數的分類規程與建立,皆無考慮類別結構,故我們提出一個新方法,藉由核心技術來推展成正比例勝算邏輯斯迴歸模型於分類例子用於次序性的類別 (例如:癌症階段或等級)。最後,以模擬方式和公開的微陣列資料集來比較分類新方法的優劣。

Microarray has demonstrated useful applications in cancer research. By analyzing the array generated gene expression data, cancers are distinguished by their molecular variations. In this paper, the multiclass cancer classification by using microarray data is addressed. In contrast to most existing classification procedures established without considering the class structure, we propose a new method by applying the kernel technique to generalize the proportional odds logistic regression for categorizing examples into ordered classes (e.g., cancer stages or grades). The performance of resulting classifier is demonstrated on simulated and publicly available microarray datasets.
URI: http://hdl.handle.net/11455/17864
其他識別: U0005-2506200722205400
Appears in Collections:應用數學系所

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