Multinomial event naive Bayesian modeling for SAGE data classification |
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Authors: | Xin Jin Wengang Zhou Rongfang Bie |
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Institution: | (1) College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China;(2) Department of Computer Science, ZhouKou Normal University, ZhouKou, 466001, China |
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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.
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Keywords: | Data mining Classification SAGE data |
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