Temporal association rules for gene regulatory networks

Abstract

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. A great challenge in the bioinformatics field is to discover gene interactions from such measurements and estimate gene networks. In this paper, we exploit data mining techniques for discovering interactions among genes based on multiple expression measurements. We present an application of the Apriori algorithm to extract temporal association rules from gene expression data. Furthermore, we address the problem of real value discretization by using both fixed thresholds and clustering techniques. Finally, we estimate the value of each rule by means of an appropriate quality index. Preliminary experimental results on Saccharomyces cerevisiae cell cycle gene expression data show the effectiveness of the proposed method.