Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/bth312
Title: Mining gene expression data for positive and negative co-regulated gene clusters
Authors: Ji, L.
Tan, K.-L. 
Issue Date: 2004
Source: 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.
Source Title: Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39861
ISSN: 13674803
DOI: 10.1093/bioinformatics/bth312
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