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  • Analysis of Mouse Periodic Gene Expression Data Based on Singular Value Decomposition and Autoregressive Modeling

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    Author(s)
    Tang, Tsz Yan
    Liew, Wee Chung
    Yan, Hong
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2010
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    Abstract
    Each DNA microarray experiment generates a large amount of gene expression profiles and it remains a challenge for biologists to robustly identify periodic gene expression profiles with certain noise level in the data. In this paper, we propose a new scheme with noise filtering technique to analyze the periodicity of gene expression base on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectrum estimation. With the combination of these methods, noise can be filtered out and over 85% of periodic gene expression can be identified in mouse presomitic mesoderm ...
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    Each DNA microarray experiment generates a large amount of gene expression profiles and it remains a challenge for biologists to robustly identify periodic gene expression profiles with certain noise level in the data. In this paper, we propose a new scheme with noise filtering technique to analyze the periodicity of gene expression base on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectrum estimation. With the combination of these methods, noise can be filtered out and over 85% of periodic gene expression can be identified in mouse presomitic mesoderm transcriptome data set.
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    Conference Title
    INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS (IMECS 2010), VOLS I-III
    Publisher URI
    http://www.iaeng.org/IMECS2010/
    Copyright Statement
    © 2010 International Association of Engineers (IAENG). The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Information and Computing Sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/37700
    Collection
    • Conference outputs

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