Multinomial event naive Bayesian modeling for SAGE data classification

  • Authors:

  • Xin Jin;Wengang Zhou;Rongfang Bie

  • Affiliations:

  • College of Information Science and Technology, Beijing Normal University, Beijing, China 100875;Department of Computer Science, ZhouKou Normal University, ZhouKou, China 466001;College of Information Science and Technology, Beijing Normal University, Beijing, China 100875

  • Venue:
  • Computational Statistics
  • Year:
  • 2007

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Abstract

Recently developed SAGE technology enables us to simultaneously quantify the expression levels of thousands of genes in a population of cells. SAGE data is helpful in classification of different types of cancers. However, one main challenge in this task is the availability of a smaller number of samples compared to huge number of genes, many of which are irrelevant for classification. Another main challenge is that there is a lack of appropriate statistical methods that consider the specific properties of SAGE data. We propose an efficient solution by selecting relevant genes by information gain and building a multinomial event model for SAGE data. Promising results, in terms of accuracy, were obtained for the model proposed.