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    Quantifying gene relatedness via nonlinear prediction of gene

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    7003403.pdf (2.324Mb)
    Date
    2006-02-21
    Author
    Dougherty, Edward R.
    Kim, Seungchan
    Bittner, Michael L.
    Chen, Yidong
    Metadata
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    Abstract
    Relatedness between genes is quantified by constructing nonlinear models predicting gene expression. Effectiveness of the model is evaluated to provide a measurement of the relatedness of genes associated with the model. Various types of models, including full-logic or neural networks can be constructed. A graphical user interface presents results of the analysis to allow evaluation by a user. Each gene's contribution to the measurement of relatedness can be shown on a graph, and graphical representations of models used to predict gene expression can be displayed.
    URI
    http://hdl.handle.net/1969.1/176810
    Subject
    702/19
    435/6.13
    702/20
    706/15
    706/21
    G16B 40/00
    G16B 25/00
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    • Texas A&M University Patents
    Citation
    Dougherty, Edward R.; Kim, Seungchan; Bittner, Michael L.; Chen, Yidong (2006). Quantifying gene relatedness via nonlinear prediction of gene. United States. Patent and Trademark Office; Texas A&M University. Libraries. Available electronically from http : / /hdl .handle .net /1969 .1 /176810.

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