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  • Finding edging genes from microarray data

    Author(s)
    An, Jiyuan
    Chen, Yi-Ping Phoebe
    Griffith University Author(s)
    An, Jay
    Year published
    2008
    Metadata
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    Abstract
    Motivation: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, ...
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    Motivation: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs. Result: We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm. Availability: The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/not, vert, similarphoebe/microarray.html.
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    Journal Title
    Journal of Biotechnology
    Volume
    135
    Issue
    3
    Publisher URI
    http://www.elsevier.com/locate/jbiotec
    DOI
    https://doi.org/10.1016/j.jbiotec.2008.04.004
    Subject
    Bioinformatics
    Biological Sciences
    Engineering
    Technology
    Publication URI
    http://hdl.handle.net/10072/26920
    Collection
    • Journal articles

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