An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data
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| Title: | An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data |
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| Authors: | Wu, Li-Ching1, Huang, Jhih-Long2, Horng, Jorng-Tzong2,3 horng@db.csie.ncu.edu.tw, Huang, Hsien-Da4 |
| Source: | Expert Systems with Applications. Mar2010, Vol. 37 Issue 3, p2202-2213. 12p. |
| Abstract: | 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 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. [Copyright &y& Elsevier] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 45544739 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Li-Ching%22">Wu, Li-Ching</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Jhih-Long%22">Huang, Jhih-Long</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Horng%2C+Jorng-Tzong%22">Horng, Jorng-Tzong</searchLink><relatesTo>2,3</relatesTo><i> horng@db.csie.ncu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Hsien-Da%22">Huang, Hsien-Da</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Mar2010, Vol. 37 Issue 3, p2202-2213. 12p. – Name: Abstract Label: Abstract Group: Ab Data: 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 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. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2009.07.053 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2202 Titles: – TitleFull: An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Li-Ching – PersonEntity: Name: NameFull: Huang, Jhih-Long – PersonEntity: Name: NameFull: Horng, Jorng-Tzong – PersonEntity: Name: NameFull: Huang, Hsien-Da IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Expert Systems with Applications Type: main |
| ResultId | 1 |
