TUMOR CLASSIFICATION BASED ON INDEPENDENT COMPONENT ANALYSIS
Chun-Hou Zheng, Yan Chen, Xiuxia Li, Yixue Li, Yunping Zhu · International Journal of Pattern Recognition and Artificial Intelligence · 2006
This paper proposes a new method for tumor classification using gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are selected using t-statistics. Secondly, the selected genes are modeled by Independent Component Analysis (ICA). Finally, Support Vector Machine (SVM) is used to classify the modeling data. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.