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Outlier analysis for gene expression data

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Abstract

The rapid developments of technologies that generate arrays of gene data enable a global view of the transcription levels of hundreds of thousands of genes simultaneously. The outlier detection problem for gene data has its importance but together with the difficulty of high dimensionality. The sparsity of data in high-dimensional space makes each point a relatively good outlier in the view of traditional distance-based definitions. Thus, finding outliers in high dimensional data is more complex. In this paper, some basic outier analysis algorithms are discussed and a new genetic algorithm is presented. This algorithm is to find best dimension projections based on a revised cell-based algorithm and to give explantations to solutions. It can solve the outlier detection problem for gene expression data and for other high dimensional data as well.

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Authors and Affiliations

  1. National High Performance Computational Center, University of Science and Technology of China, 230027, Hefei, P.R. China

    Chao Yan, Guo-Liang Chen & Yi-Fei Shen

Authors
  1. Chao Yan
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  2. Guo-Liang Chen
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  3. Yi-Fei Shen
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Additional information

This work was supported by the National High-Tech Development 863 Program of China under the Grant No.2001AA111041 2002AA104560, and High-Standard Universities Construction Project in CAS.

Chao Yan is a Ph.D. candidate of Dept. of Computer Sci. & Tech. University of Science and Technology of China. His major interests include cluster, bioinformatics and algorithm.

Guo-Liang Chen is a professor, academician of the Chinese Academy of Sciences. He works with the Dept. of Computer Sci. & Tech., University of Science and Technology of China. His major Research areas include parallel theory and algorithm.

Yi-Fei Shen is a Ph.D. candidate of Dept. of Computer Sci. & Tech., University of Science and Technology of China. His major interests include bioinformatics and algorithm.

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Yan, C., Chen, GL. & Shen, YF. Outlier analysis for gene expression data. J. Comput. Sci. & Technol. 19, 13–21 (2004). https://doi.org/10.1007/BF02944782

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  • DOI: https://doi.org/10.1007/BF02944782

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