Recent advances in Microarray technologies have encouraged to extract gene regulatory network from microarray data in order to understand the gene regulation (in terms of activators and inhibitors) from time-series gene expression patterns in a cell. The concept of positive and negative co-regulated gene clusters (pncgc)[1] Association Rule Mining is used to analyze the gene expression data that more accurately reflects the co-regulations of genes than the existing methods which are computationally expensive.
Experiments were performed with Saccharomyces cerevisiae and Homo Sapiens dataset through which semi co-regulated gene clusters and positive and negative co-regulated gene clusters were extracted. The resulting semi co-regulated gene clusters were used in inferring a gene regulatory network which was compared with large scale regulatory network inferred from modified association rule mining algorithm. The usage of positive and negative co-regulated gene cluster approach of identifying the network outperformed the modified association rule mining [2], especially when analyzing large numbers of genes.