Mining gene expression data for positive and negative co-regulated gene clusters.
BACKGROUND
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.


BACKGROUND
The software is available upon request.
Ji
Liping
L
Department Computer Science, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore. jiliping@comp.nus.edu.sg
Tan
Kian-Lee
KL
D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D016247:Information Storage and Retrieval / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013223:Statistics as Topic; D015003:Yeasts / Q000235:genetics