Abstract
The article presents the results of research concerning development of inductive algorithm for hierarchical Bayesian clustering of gene expression of patients with two types of brain tumors and healthy individuals. The study carried out comparative studies of the clustering quality of inductive and classical methods of Bayesian hierarchical clustering algorithm (BHC). It is proposed to apply the moving average and FFT filtering methods for data dimension reducing. The basic principles of creating an inductive model of objective clustering are formed, the results of clustering are shown at various levels of data dimension reducing, the advantages of objective clustering BHC in comparison with the canonical BHC algorithm are determined.KeywordsGlioblastomaBrain tumorOligodendroglial tumorsGlioblastoma multiforme profiling of gene expressionInductive modelingBayesian hierarchical algorithmExternal balance criterionData dimension reducingNormalizingExploratory analysis criterionGene expression profiles
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