| New feature extraction in gene expression data for tumor classification |
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| 作者姓名： | HE Reny CHENG Qiansheng WU Lianwen YUAN Kehong |
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| 作者单位： | LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China,LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China,LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China,LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China |
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| 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.
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| 关 键 词： | tumor classification support vector machine (SVM) bioinformatics feature extraction gene expression. |
New feature extraction in gene expression data for tumor classification |
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| HE Reny,CHENG Qiansheng,WU Lianwen,YUAN Kehong.New feature extraction in gene expression data for tumor classification[J].Progress in Natural Science,2005,15(9):861-864. |
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| Authors: | HE Renya CHENG Qiansheng WU Lianwen YUAN Kehong |
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| Institution: | LMAM, School of Mathematical Sciences, Institute of Molecular Medicine, Peking University, Beijing 100871, China |
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| Abstract: | 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. |
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| Keywords: | tumor classification support vector machine (SVM) bioinformatics feature extraction gene expression |
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