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作者:

Liu, Quan-Jin (Liu, Quan-Jin.) | Li, Ying-Xin (Li, Ying-Xin.) | Ruan, Xiao-Gang (Ruan, Xiao-Gang.)

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摘要:

In this paper we proposed an approach for tumor informative genes selection by analysis of gene sensitivity based on SVM. We analyzed the gene expression profiles of colon and recursively eliminated the genes which have lower sensitivity to SVM, then a set of candidate nested feature subsets were generated. Support Vector Machines were employed to classify the samples using these candidate feature subsets, and the feature subset with a minimum error was chosen as a set of colon informative genes. The results show that this feature subset contains more tumor classification information than other feature subsets identified in the literatures. The method proposed in this paper is feasible and effective.

关键词:

Classification (of information) Feature extraction Gene expression Sensitivity analysis Support vector machines Tumors

作者机构:

  • [ 1 ] [Liu, Quan-Jin]Department of Physics, Anqing Teacher's College, Anqing 246011, China
  • [ 2 ] [Liu, Quan-Jin]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 3 ] [Li, Ying-Xin]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 4 ] [Li, Ying-Xin]CCD Item, Beijing Jingwei Textile Machinery New Technology Co. Ltd., Beijing 100176, China
  • [ 5 ] [Ruan, Xiao-Gang]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

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来源 :

Journal of Beijing University of Technology

ISSN: 0254-0037

年份: 2007

期: 9

卷: 33

页码: 954-958

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WoS核心集被引频次: 0

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