Gene expression data classification using consensus independent component analysis

Zheng CH; Huang DS; Kong XZZhao XM

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Gene expression data classification using consensus independent component analysis

机译：Gene expression data classification using consensus independent component analysis

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摘要

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis (ICA). Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

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《Genomics, proteomics & bioinformatics》 |2008年第2期|74-82|共9页
作者
Zheng CH; Huang DS; Kong XZZhao XM;
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作者单位

Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;

College of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China.;

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正文语种英语
中图分类生物科学;
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Gene expression data classification using consensus independent component analysis

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