Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/bti026
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dc.titleIdentifying time-lagged gene clusters using gene expression data
dc.contributor.authorJi, L.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T07:51:16Z
dc.date.available2013-07-04T07:51:16Z
dc.date.issued2005
dc.identifier.citationJi, L., Tan, K.-L. (2005). Identifying time-lagged gene clusters using gene expression data. Bioinformatics 21 (4) : 509-516. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/bti026
dc.identifier.issn13674803
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39860
dc.description.abstractMotivation: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. Results: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. © Oxford University Press 2004; all rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1093/bioinformatics/bti026
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1093/bioinformatics/bti026
dc.description.sourcetitleBioinformatics
dc.description.volume21
dc.description.issue4
dc.description.page509-516
dc.description.codenBOINF
dc.identifier.isiut000227127000012
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