What is Gene Expression Microarrays

The advent of genomic microarray technology enables simultaneously measuring the expressions of thousands of genes in massive experiments, and hence provides scientists, for the first time, the opportunity of observing complex relationships between various genes in a genome. In order to extract biologically meaningful insights from a plethora of data generated from microarray experiments, knowledge discovery techniques, which discover patterns, statistical or predictive models, and relationships among massive data, have been widely applied in microarray data analysis. For example, clustering can be applied to identify groups of genes that are regulated in a similar manner under a number of experimental conditions or groups of samples that show similar expression patterns across a number of genes (Jiang, Tang, & Zhang, 2004). Classification can be performed to characterize the cellular difference between different samples, such as between normal and cancer cells or between cancer cells with different responses to treatment, and can potentially be used to predict the classes of samples based on their gene expression patterns (Statnikov, Aliferis, Tsamardinos, Hardin, & Levy, 2005). Feature selection or gene selection can help identify among thousands of genes a small fraction of genes that are relevant for discriminating between different sample types, and may potentially lead to the identification of a few biologically relevant “marker” genes for subsequent biological validation (Saeys, Inza, & Larranaga, 2007). This article provides a brief introduction to the field of knowledge discovery and its applications in discovering useful knowledge from genomic microarray data. It describes common knowledge discovery tasks for genomic microarray data, presents representative methods for each task, and identifies emerging challenges and trends in knowledge discovery from genomic microarray data.