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Among the great amount of genes presented in microarray gene expression data, only a small fraction is effective for performing a certain diagnostic test. In this regard, mutual information has been shown to be successful for selecting a set of relevant and nonredundant genes from microarray data. However, information theory offers many more measures such as the f-information measures that may be...
Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis that deals with few samples and large features. In this paper, a new ensemble method for cancer data classification is proposed. The gene expression data is firstly preprocessed for normalization. Correlation analysis method...
Support vector machines are applied to extract marker genes from various microarray data sets: breast cancer, leukemia and monocyte - macrophage differentiation to ease classification of related pathologies or characterize related gene regulation pathways.
The set of gene micro-arrays, which consists of two leukemia types, was used as a target to evaluate the efficiency of novel integrated data mining classification process. Discovering the most relevant subset of genes among few thousands of analyzed genes is necessary to get accurate disease classification. Dimensional complexity of the classification process was reduced by a filter based on mutual...
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