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http://dx.doi.org/10.5391/JKIIS.2012.22.2.192

Disease Classification using Random Subspace Method based on Gene Interaction Information and mRMR Filter  

Choi, Sun-Wook (인하대학교 정보통신공학과)
Lee, Chong-Ho (인하대학교 정보통신공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.2, 2012 , pp. 192-197 More about this Journal
Abstract
With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.
Keywords
Gene Interaction; Signaling Pathway; Ensemble Classifier; mRMR; Random Subspace Method;
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