Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data

doi:10.1109/CISIS.2009.70

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 會議論文 > Item 987654321/75818

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题名:	Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data
作者:	Yang, Andy C.;Hsu, Hui-huang;Lu, Ming-da
贡献者:	淡江大學資訊工程學系
关键词:	Gene Expression Analysis;Gene Regulation Identification;Microarray;Outlier Filtering;Time-Series Data
日期:	2009-03
上传时间:	2012-04-17 11:27:07 (UTC+8)
出版者:	N.Y.: IEEE (Institute of Electrical and Electronic Engineers)
摘要:	Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. Time-series microarray data are gene expression values generated from microarray experiments within certain time intervals. Scientists can infer gene regulations in a biological system by judging whether two genes present similar gene expression values in microarray time-series data. Recently, a great many methods are widely applied on microarray time-series data to find out the similarity and the correlation degree among genes. Existing approaches including traditional Pearson coefficient correlation, Bayesian networks, clustering analysis, classification methods, and correlation analysis have individual disadvantages such as high computational complexity or they may be unsuitable for some microarray data. Traditional Pearson correlation coefficient is a numeric measuring method which gives novel effectiveness on two sets of numeric data. However, it is not suitable to be applied on microarray time-series data because of the existence of outliers among gene expression values. This paper presents a novel method of applying Pearson correlation coefficient along with an outlier filtering procedure on the widely-used microarray time-series datasets. Results show that the proposed method produces a better outcome compared with traditional Pearson correlation coefficient on the same dataset. Results show that the proposed method not only can find out certain more known regulatory gene pairs, but also keeps rational computational time.
關聯:	Proceedings of the Third International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2009), pp.854-859
DOI:	10.1109/CISIS.2009.70
显示于类别:	[資訊工程學系暨研究所] 會議論文

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