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Gene Expression Data Analysis Using Parallel Processor based Pattern Classification Method  

Choi, Sun-Wook (Dept. of Information Engineering, Inha University)
Lee, Chong-Ho (Dept. of Information Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.46, no.6, 2009 , pp. 44-55 More about this Journal
Abstract
Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.
Keywords
hypernetwork model; microarray; gene expression; pattern classification; parallel processing;
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