Discovering indirect Gene associations by Filtering-Based Indirect Association Rule Mining

Data mining is a popular technology used for microarray analysis. Using this technique*biologists can effectively elucidate gene expression data. In this research*we propose the FIARM (Filtering-Based Indirect Association Rule Mining) algorithm to analyze gene microarray data. The form 〈X; Y{divides}M〉 is used to present the indirect relation of X and Y*which depends on M. This signifies that both gene X and gene M are likely involved in a given biological activity. Furthermore*both gene Y and gene M likely join together to carry out another biological activity. As gene M is the necessary factor in these different biological activities*it can help biologists determine gene relationships in diverse activities. We use semantic similarity of Gene Ontology to verify the accuracy of discovered gene relations. Under experimental evaluation*the proposed method can discover the relationship dissimilated by association rules to effectively assist biologists in complicated genetic research.