Tumor Classification Using Non-negative Matrix Factorization

  • Ping Zhang
  • Chun-Hou Zheng
  • Bo Li
  • Chang-Gang Wen
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

Abstract

With the advent of DNA microarrys, it is now possible to use the microarry data for tumor classification. Yet previous works have not use the nonnegative information of gene expression data for classification. In this paper, we propose a new method for tumor classification using gene expression data. In this method, we first extract new features of the gene expression data by virtue of non-negative matrix factorization (NMF) and its extension, i.e. sparse NMF (SNMF) then apply support vector machines (SVM) to classify the tumor samples using the extracted features. To better fit for classification aim, a new SNMF algorithm is also proposed.

Keywords

Gene expression data Non-negative matrix factorization SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ping Zhang
    • 1
  • Chun-Hou Zheng
    • 2
    • 3
  • Bo Li
    • 3
  • Chang-Gang Wen
    • 2
  1. 1.Institute of automationQufu Normal UniversityRizhaoChina
  2. 2.College of Information and Communication TechnologyQufu Normal University 
  3. 3.Intelligent Computing Lab, Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina

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