A comparative study of feature selection methods for probabilistic neural networks in cancer classification

Accurate diagnosis and classification is the key issue for the optimal treatment of cancer patients. Several studies demonstrate that cancer classification can be estimated with high accuracy, sensitivity and specificity from microarray-based gene expression profiling using artificial neural networks. In this paper, a comprehensive study was undertaken to investigate the capability of the probabilistic neural networks (PNN) associated with a feature selection method, a so-called signal-to-noise statistic, in the application of cancer classification. The signal-to-noise statistic, which represents the correlation with the class distinction, is used to select the marker genes and trim the dimension of data samples for the PNN. The experimental results show that the association of the probabilistic neural network with the signal-to-noise statistic can achieve superior classification results for two types of acute leukemias and five categories of embryonal tumors of central nervous system with satisfactory computation speed. Furthermore, the signal-to-noise statistic analysis provides candidate genes for future study in understanding the disease process and the identification of potential targets for therapeutic intervention.