Cancer diagnosis based on the analysis of microarray data
Journal Title: Science Paper Online - Year 2010, Vol 5, Issue 2
Abstract
Small sample and high dimension are the features of gene expression, which results in redundant genes. Plenty of redundant genes will not only reduce the diagnosis accuracy, but also will increase the computation burden. Therefore it is necessary to select related genes to improve cancer diagnosis accuracy. In order to solve the high-dimensional problem, the rank sum test is adopted to choose related genes, and then K-means clustering and fuzzy C-means clustering algorithms are used to form the diagnosis models respectively. Experimental results involving two public DNA microarray datasets indicate that the proposed diagnosis models both have high classification accuracy, which can provide an assistant manner for cancer diagnosis.
Authors and Affiliations
Qingfeng Liu, Ruhai Lei
Keywords
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