Kernel based gene expression pattern discovery and its application on cancer classification

Authors:
Ruichu Cai;Zhifeng Hao;Wen Wen;Han Huang
Affiliations:
Faculty of Computer Science, Guangdong University of Technology, 510006 Guangzhou, PR China and School of Computer Science and Engineering, South China University of Technology, 510640 Guangzhou, ...;Faculty of Computer Science, Guangdong University of Technology, 510006 Guangzhou, PR China;Faculty of Computer Science, Guangdong University of Technology, 510006 Guangzhou, PR China;School of Software Engineering, South China University of Technology, 510640 Guangzhou, PR China and State Key Laboratory for Novel Software Technology, Nanjing University, 210093 Nanjing, PR Chin ...
Venue:
Neurocomputing
Year:
2010

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Abstract

Association rules have been widely used in gene expression data analysis. However, there is no systematical way to select interesting rules from the millions of rules generated from high dimensional gene expression data. In this study, a kernel density estimation based measurement is proposed to evaluate the interestingness of the association rules. Several pruning strategies are also devised to efficiently discover the approximate top-k interesting patterns. Finally, over-fitting problem of the classification model is addressed by using conditional independence test to eliminate redundant rules. Experimental results show the effectiveness of the proposed interestingness measure and classification model.