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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 [<jats:italic>gene A<\/jats:italic>\u2191, <jats:italic>gene B<\/jats:italic>\u2193] \u2192 (7 min) [<jats:italic>gene C<\/jats:italic>\u2191], which represents that high expression level of <jats:italic>gene A<\/jats:italic> and significant repression of <jats:italic>gene B<\/jats:italic> followed by significant expression of <jats:italic>gene C<\/jats:italic> after 7 minutes. The proposed TARM method is tested with <jats:italic>Saccharomyces cerevisiae<\/jats:italic> cell cycle time-series microarray gene expression data set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In the parameter fitting phase of TARM, the fitted parameter set [threshold = \u00b1 0.8, support \u2265 3 transactions, confidence \u2265 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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-10-s3-s6","type":"journal-article","created":{"date-parts":[[2009,3,19]],"date-time":"2009-03-19T19:14:33Z","timestamp":1237490073000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Identification of temporal association rules from time-series microarray data sets"],"prefix":"10.1186","volume":"10","author":[{"given":"Hojung","family":"Nam","sequence":"first","affiliation":[]},{"given":"KiYoung","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Doheon","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2009,3,19]]},"reference":[{"issue":"9","key":"3279_CR1","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1093\/bioinformatics\/bti251","volume":"21","author":"DW Kim","year":"2005","unstructured":"Kim DW, Lee KH, Lee D: Detecting clusters of different geometrical shapes in microarray gene expression data. 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