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  • Classification of Short Human Exons and Introns based on Statistical Features

    Author(s)
    Wu, YH
    Liew, AWC
    Yan, H
    Yang, MS
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2003
    Metadata
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    Abstract
    The classification of human gene sequences into exons and introns is a difficult problem in DNA sequence analysis. In this paper, we define a set of features, called the simple Z (SZ) features, which is derived from the Z-curve features for the recognition of human exons and introns. The classification results show that SZ features, while fewer in numbers ~three in total!, can preserve the high recognition rate of the original nine Z-curve features. Since the size of SZ features is one-third of the Z-curve features, the dimensionality of the feature space is much smaller, and better recognition efficiency is achieved. If the ...
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    The classification of human gene sequences into exons and introns is a difficult problem in DNA sequence analysis. In this paper, we define a set of features, called the simple Z (SZ) features, which is derived from the Z-curve features for the recognition of human exons and introns. The classification results show that SZ features, while fewer in numbers ~three in total!, can preserve the high recognition rate of the original nine Z-curve features. Since the size of SZ features is one-third of the Z-curve features, the dimensionality of the feature space is much smaller, and better recognition efficiency is achieved. If the stop codon feature is used together with the three SZ features, a recognition rate of up to 92% for short sequences of length ,140 bp can be obtained.
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    Journal Title
    Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)
    Volume
    67
    Issue
    6
    Publisher URI
    http://prola.aps.org/
    DOI
    https://doi.org/10.1103/PhysRevE.67.061916
    Subject
    Mathematical Sciences
    Physical Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/21795
    Collection
    • Journal articles

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