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Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze <jats:italic>in vivo<\/jats:italic> rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-279","type":"journal-article","created":{"date-parts":[[2010,5,26]],"date-time":"2010-05-26T06:14:44Z","timestamp":1274854484000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat liver"],"prefix":"10.1186","volume":"11","author":[{"given":"Tung T","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Richard R","family":"Almon","sequence":"additional","affiliation":[]},{"given":"Debra C","family":"DuBois","sequence":"additional","affiliation":[]},{"given":"William J","family":"Jusko","sequence":"additional","affiliation":[]},{"given":"Ioannis P","family":"Androulakis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,5,26]]},"reference":[{"issue":"1","key":"3736_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.2165\/00822942-200504010-00004","volume":"4","author":"N Altman","year":"2005","unstructured":"Altman N: Replication, variation and normalisation in microarray experiments. 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