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This article is part of the supplement: Second International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2008

Open Access Proceedings

Identification of temporal association rules from time-series microarray data sets

Hojung Nam1, KiYoung Lee2 and Doheon Lee1*

Author Affiliations

1 Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea

2 Department of Bioengineering University of California at San Diego, La Jolla, California 92093, USA

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BMC Bioinformatics 2009, 10(Suppl 3):S6  doi:10.1186/1471-2105-10-S3-S6

Published: 19 March 2009

Abstract

Background

One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A↑, gene B↓] → (7 min) [gene C↑], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.

Results

In the parameter fitting phase of TARM, the fitted parameter set [threshold = ± 0.8, support ≥ 3 transactions, confidence ≥ 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.

Conclusion

In this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.