56 286

Cited 20 times in

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

Authors
Nam, H; Lee, K; Lee, D
Citation
BMC bioinformatics, 10(suppl.3):S6-S6, 2009
Journal Title
BMC bioinformatics
ISSN
1471-2105
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 upward arrow, gene B downward arrow] --> (7 min) [gene C upward arrow], 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 >or= 3 transactions, confidence >or= 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.
MeSH terms
Bayes TheoremComputational Biology/methods*Databases, GeneticGene Expression Profiling/methods*Oligonucleotide Array Sequence Analysis/methods*Saccharomyces cerevisiae/geneticsSaccharomyces cerevisiae/metabolism
DOI
10.1186/1471-2105-10-S3-S6
PMID
19344482
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Medical Informatics
AJOU Authors
이기영
Full Text Link
Files in This Item:
1471-2105-10-S3-S6.pdfDownload
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse