Gene selection for cancer clustering analysis based on expression data

Although the genomics data are accumulated in an exponential growth, the molecular complexity of cancer is still hard to understand. The most remarkable characteristics of the genomic data are severely high-dimensional features with a small number of samples, such as gene expression data. The traditional data mining method has a limited ability to process these asymmetry datasets. In order to select the key genes from the high-dimensional gene expression data for cancer clustering analysis, gene selection based on proportional hazard model is applied in this paper. The proportional hazard model is a statistical approach used for survival risk analysis. The significant genes are selected for clustering analysis based on gene expression dataset. We demonstrate the effectiveness of this method on breast cancer and lung cancer. The experiments show a better cancer clustering result of separating of samples into distinct subclasses.