An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data

NCU Institutional Repository > 生醫理工學院 > 系統生物與生物資訊研究所 > 期刊論文 > Item 987654321/51066

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/51066

题名:	An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data
作者:	Wu,LC;Huang,JL;Horng,JT;Huang,HD
贡献者:	系統生物與生物資訊研究所
关键词:	SACCHAROMYCES-CEREVISIAE;IDENTIFICATION
日期:	2010
上传时间:	2012-03-27 18:20:23 (UTC+8)
出版者:	國立中央大學
摘要:	Motivation: The analysis of time series gene expression data can provide us with the opportunity to find co-regulated genes that show a similar expression patterns under a contiguous subset of experimental conditions. However, these co-regulated genes may behave almost independently under other conditions. Furthermore, the similarity in the expression pattern might be time-shifted. In that case, we need to be concerned with grouping genes that share similar expression patterns under a contiguous subset of conditions and where the similarity in expression pattern might have time lags. in addition, to be considered a time-shifted similar pattern, because co-regulated genes in a biological process may show a periodic pattern in their cell cycle expression, we also should group genes with periodic similar patterns over multiple cell cycles. If this is carried out, we can regard such grouped genes as cell-cycle regulated genes. Results: We propose a method that follows the q-cluster concept [Ji, L., & Tan, K. L. (2005). Identifying time-lagged gene clusters using gene expression data. Bioinformatics, 21(4), 509-516] and further advances this approach towards the identification of cell-cycle regulated genes using cell cycle microarray data. We used our method to Cluster a microarray time series of yeast genes to assess the statistically biological significance of the obtained clusters we used the p-value obtained from the hypergeometric distribution. We found that several clusters provided findings suggesting a TF-target relationship. In order to test whether our method could group related genes that other methods have found difficult to group, we compared our method with other measures such as Spearman Rank Correlation and Pearson Correlation. The results of the comparison demonstrate that our method indeed could group known related genes that these measures regard as having only a weak association. (C) 2009 Elsevier Ltd. All rights reserved.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[系統生物與生物資訊研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	640	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....