Please use this identifier to cite or link to this item:
|Title:||Mining gene expression data for positive and negative co-regulated gene clusters|
|Citation:||Ji, L., Tan, K.-L. (2004). Mining gene expression data for positive and negative co-regulated gene clusters. Bioinformatics 20 (16) : 2711-2718. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/bth312|
|Abstract:||Motivation: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. Results: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. © Oxford University Press 2004; all rights reserved.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 17, 2018
WEB OF SCIENCETM
checked on Aug 8, 2018
checked on Jul 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.