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

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Accession number:20094712485287

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

Authors:Wu, Li-Ching (1); Huang, Jhih-Long (2); Horng, Jorng-Tzong (2); Huang, Hsien-Da (3)

Author affiliation:(1) Institute of System Biology and Bioinformatics, National Central University, Taiwan; (2) Department of Computer Science and Information Engineering, National Central University, Taiwan; (3) Institute of Bioinformatics, National Chiao-Tung

University, Taiwan; (4) Department of Bioinformatics, Asia University, Taiwan

Corresponding author:Horng, J.-T.

([email protected])

Source title:Expert Systems with Applications Abbreviated source title:Expert Sys Appl Volume:37

Issue:3

Issue date:March 15, 2010 Publication year:2010 Pages:2202-2213 Language:English ISSN:09574174 CODEN:ESAPEH

Document type:Journal article (JA)

Publisher:Elsevier Ltd, Langford Lane, Kidlington, Oxford, OX5 1GB, United Kingdom

Abstract: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

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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. © 2009 Elsevier Ltd. All rights reserved.

Number of references:16

Main heading:Time series analysis

Controlled terms:Bioactivity - Bioinformatics - Data mining - Expert systems - Gene expression - Time series

Uncontrolled terms:Biological process - Biological significance - Cell cycle - Co-regulated genes - Experimental conditions - Expression data - Expression patterns - Gene clusters - Gene Expression Data - Microarray data - Multiple cells - P-values - Pearson correlation - Periodic pattern - Regulated genes - Similar pattern - Spearman rank correlation - Time lag - Time-series gene expression data

Classification code:922.2 Mathematical Statistics - 903 Information Science - 723.4.1 Expert Systems - 723.3 Database Systems - 723.2 Data Processing and Image Processing - 461.9 Biology - 461.8.2 Bioinformatics - 461.8.1 Genetic Engineering - 461.6 Medicine and Pharmacology

DOI:10.1016/j.eswa.2009.07.053 Database:Compendex

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