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—Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label.(More)
The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways; we increased the number of samples by integrating independently(More)
Microarrays produce expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification. We performed a direct integration of individual microarrays with same biological objectives by converting an expression value into a rank value within a sample and built a classifier based on rank comparison. Our classifier is(More)
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