Gene Expression Data Classification Using Independent Variable Group Analysis

Authors:
Chunhou Zheng;Lei Zhang;Bo Li;Min Xu
Affiliations:
College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China 276826 and Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sci ...;Biometric Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China 230031;College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China 276826
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method, we first used t-statistics method to select a part of genes from the original data. Then we selected the key genes from the selected genes by t-statistics for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.