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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Jin, X., Zhou, W. & Bie, R. Multinomial event naive Bayesian modeling for SAGE data classification. Computational Statistics 22, 133–143 (2007). https://doi.org/10.1007/s00180-007-0029-0
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DOI: https://doi.org/10.1007/s00180-007-0029-0