کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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505578 | 864520 | 2009 | 4 صفحه PDF | دانلود رایگان |
Gene expression datasets is a means to classify and predict the diagnostic categories of a patient. Informative genes and representative samples selection are two important aspects for reducing gene expression data. Identifying and pruning redundant genes and samples simultaneously can improve the performance of classification and circumvent the local optima problem. In the present paper, the modified particle swarm optimization was applied to selecting optimal genes and samples simultaneously and support vector machine was used as an objective function to determine the optimum set of genes and samples. To evaluate the performance of the new proposed method, it was applied to three publicly available microarray datasets. It has been demonstrated that the proposed method for gene and sample selection is a useful tool for mining high dimension data.
Journal: Computers in Biology and Medicine - Volume 39, Issue 7, July 2009, Pages 646–649