New feature extraction in gene expression data for tumor classification

作     者：HE Renya, CHENG Qiansheng**, WU Lianwen and YUAN Kehong(LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China) 

作者机构：LMAM School of Mathematical Sciences Institute of Molecular Medicine Beijing 100871 Peking University China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2005年第15卷第9期

页      面：861-864页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：SupportedbyNationalNaturalScienceFoundationofChina(GrantNos.69872003and40035010) 

主　　题：Bioinformatics Feature extraction Gene expression Support vector machine (SVM) Tumor classification 

摘      要：Using gene expression data to discriminate tumor from the normal ones is a powerful method. However, it is sometimes difficult because the gene expression data are in high dimension and the object number of the data sets is very small. The key technique is to find a new gene expression profiling that can provide understanding and insight into tumor related cellular processes. In this paper, we propose a new feature extraction method based on variance to the center of the class and employ the support vector machine to recognize the gene data either normal or tumor. Two tumor data sets are used to demonstrate the effectiveness of our methods. The results show that the performance has been significantly improved.