由細胞週期表現資料中尋找延遲調控的基因; Identifying co-regulated gene group from time-lagged gene cluster using cell cycle expression data

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9161

Title:	由細胞週期表現資料中尋找延遲調控的基因;Identifying co-regulated gene group from time-lagged gene cluster using cell cycle expression data
Authors:	黃志龍;Jhih-Long Huang
Contributors:	資訊工程研究所
Keywords:	微陣列;相互調控基因;基因表現;細胞週期表現資料;cell cycle expression data;gene expression;microarray;co-regulated gene
Date:	2006-06-15
Issue Date:	2009-09-22 11:42:16 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	我們依循 q-cluster 方法的概念，希望能更進一步找出在細胞週期中相互調控的基因。我們應用我們的方法在酵母菌的基因表現資料上，為了驗證是否我們方法所得到的群落具有統計上的生物意義，我們透過計算超幾何分配來顯示，經過我們計算後，發現的確有某些群落與某個特殊功能有高度相關，除此之外，我們也發現有些群落對於尋找調控關係同樣有顯著的幫助。為了驗證我們的方法是否能正確將相關的基因歸類在一起，我們就跟現存的某些測量方法做比較，結果顯示我們方法的確將那些測量方法視為不相關，但卻是相關的基因歸類在一起。 We propose a method follows q-cluster’s(Ji and Tan 2005) concept and further advance in finding cell-cycle regulated genes for cell cycle microarray data. We used our method to cluster for time series Yeast gene data. To assess the statistically biological significance of the obtained clusters, we used the P-value obtained from hypergeometric distribution to reveal. We found that several clusters showed a significant enrichment of genes of a particular functional category. Besides, several clusters further facilitate findings for TF-target relationships. In order to test whether our method could group related genes which other method is hard to group together. We compare our method with some measures such as Spearman Rank Correlation, Pearson Correlation and Event Method. The result of comparison demonstrates that our method indeed could group known related genes which these measures regard as weak association among them instead.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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