A Novel Discrete Particle Swarm Optimization Algorithm for Microarray Data-Based Tumor Marker Gene Selection

Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples can lead inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a novel marker gene selection approach is proposed. Firstly, some top-ranked informative genes are selected by signal-noise ratio estimation method. Then a novel discrete particle swarm optimization (PSO) algorithm is applied to select a few marker genes and support vector machines (SVM) is used as evaluator for getting better prediction performance. Experiments show that the proposed method produces better recognition with fewer marker genes than many other methods on Colon tumor dataset. It has been demonstrated the modified discrete PSO is a useful tool for selecting marker genes and mining high dimension data.