Penalized independent component discriminant method for tumor classification

Authors:
Chun-Hou Zheng;Li Shang;Yan Chen;Zhi-Kai Huang
Affiliations:
School of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China;Department of Automation, University of Science and Technology of China, Hefei, Anhui, China;School of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China;Department of Automation, University of Science and Technology of China, Hefei, Anhui, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

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Abstract

This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.