Discovering Meaningful Rules from Gene Expression Data

Gene expression data are essential in understanding gene functions, biological networks, and cellular conditions. Over the last decade, with the use of DNA microarray technology, larger-scale gene expression data has become available and various data mining techniques have been used in an attempt to extract biologically relevant knowledge. Rule mining is a widely used approach in data mining and lately has attracted considerable interest in Bioinformatics with a number of methods being proposed over the last few years. Rule discovery can reveal biologically relevant associations between different genes or between experimental conditions and gene expression. The widely used data clustering techniques that can successfully identify co-expressed genes can be seen as a special case of rule mining. Furthermore, association rule mining can reveal temporal patterns of gene co-expression inferring participation in gene networks. This paper is a review of recent developments and current state of association rule mining methodologies in gene expression data. We discuss the advantages and limitations of the existing techniques and moreover, we propose a general framework under which association rule mining in gene expression can infer very often meaningful biological results.