ADVANCED EVOLUTIONARY ALGORITHMS FOR INTELLIGENT MICROARRAY IMAGE ANALYSIS

cDNA microarrays, one of the most fundamental and powerful biotechnology tools, is being utilized in a variety of biomedical applications as it enables scientists to simultaneously analyze the expression levels of thousands of genes over different samples. One of the most essential processes of cDNA microarray experiments is the image analysis one, which is divided into three phases, namely, gridding, spot-segmentation and intensity extraction. Although its two former phases appear relatively straightforward, they are in fact rather challenging procedures due to the nature of microarray images. For their implementation, the currently available software programs require human intervention, which significantly affects the biological conclusions reached during microarray experiments. In this chapter, the basic process of analyzing a microarray image is described and advanced evolutionary algorithms implementing the automatic gridding and segmentation processes are presented. In reality, both of these algorithms are based on optimization problems which are solved by using evolutionary genetic algorithms. Contrary to existing software systems, the proposed methods are fully automatic as they do not require any human intervention; they are also noise resistant and yield excellent results even under adverse conditions. Last but not least, they outperform other software programs as well as established techniques.