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Unsupervised Clustering of Multivariate Time Series Microarray Experiments based on Incremental Non-Gaussian Analysis

  • Ng, Kam Swee (Department of ATTD Automation Intel Products (M)) ;
  • Yang, Hyung-Jeong (Department of Computer Science Chonnam National University) ;
  • Kim, Soo-Hyung (Department of Computer Science Chonnam National University) ;
  • Kim, Sun-Hee (Department of Computer Science Carnegie Mellon University) ;
  • Anh, Nguyen Thi Ngoc (Department of Electronics and Computer Engineering Chonnam National University)
  • Received : 2012.01.09
  • Accepted : 2012.02.17
  • Published : 2012.03.28
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Abstract

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

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