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Abstract:
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In modern clinical neuro-oncology, the diagnosis and classification of malignant gliomas remains problematic, and effective therapies are still elusive. In recent years, there has been a move toward the use of cDNA microarrays for tumor classification. The classification becomes more difficult when there are more than two cancer types, as with glioma. This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial logit likelihood and the likelihood related to the multiclass Support Vector Machine (SVM) model. It is shown that our proposed Bayesian classification models with multiple shrinkage parameters can produce accurate classification scheme for the glioma cancer compared to the existing classical methods.
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