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  • Microarray Missing Data Imputation based on a Set Theoretic Framework and Biological Constraints

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    Author(s)
    Gan, Xiangchao
    Liew, Alan Wee-Chung
    Yan, Hong
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2006
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    Abstract
    Gene expressions measured using microarrays usually suffer from the missing value problem. Existing missing value imputation algorithms have some limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by a global structure. In addition, these algorithms do not take into account many biological constraints in the imputation procedure. In this paper, we propose a set theoretic framework for missing data imputation. We design our algorithm by taking into consideration the biological characteristic of the data ...
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    Gene expressions measured using microarrays usually suffer from the missing value problem. Existing missing value imputation algorithms have some limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by a global structure. In addition, these algorithms do not take into account many biological constraints in the imputation procedure. In this paper, we propose a set theoretic framework for missing data imputation. We design our algorithm by taking into consideration the biological characteristic of the data and exploit the local correlation and the global correlation structure adaptively. Experiments show that our algorithm can achieve a significant reduction of error compared with existing methods.
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    Conference Title
    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS
    Volume
    3
    DOI
    https://doi.org/10.1109/ICPR.2006.796
    Copyright Statement
    © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/24404
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    • Conference outputs

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