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The problem of marker selection in DNA microarray experiment, due to the "curse of dimensionality", has been mostly addressed so far by linear approaches. Taking into account the fact that the domain of interest is a complex one, where non-linear interconnections and dependencies may also exist among the extremely large number of examined genes, we address the use of nonlinear tools to assess...
Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on microarray data is improved. This paper presents...
Recently there is an increasing interest in changing the criterion of tumor classification from morphologic to molecular. In this perspective, the problem can be regarded as a classification problem in machine learning. In this study wavelet analysis is used to extract the features from high dimensional microarray profiles. To make it easier to find the significant genes, we remove the small change...
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