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    » Prediction of glycosylation sites using random forests
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    BMCBI
    2008
    101views more  BMCBI 2008»
    Prediction of glycosylation sites using random forests
    13 years 4 months ago
    Prediction of glycosylation sites using random forests
    Download  www.biomedcentral.com
    Background: Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the...
    Stephen E. Hamby, Jonathan D. Hirst
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    BMCBI
    2007
    82views more  BMCBI 2007»
    Glycosylation site prediction using ensembles of Support Vector Machine classifiers
    13 years 4 months ago
    Glycosylation site prediction using ensembles of Support Vector Machine classifiers
    Download  www.cs.iastate.edu
    Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, ...
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    BMCBI
    2008
    74views more  BMCBI 2008»
    Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs
    13 years 4 months ago
    Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs
    Download  www.biomedcentral.com
    Background: As one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identificat...
    Yong-Zi Chen, Yu-Rong Tang, Zhi-Ya Sheng, Ziding Z...
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    SADM
    2011
    272views Applied Computing» more  SADM 2011»
    Random survival forests for high-dimensional data
    12 years 11 months ago
    Random survival forests for high-dimensional data
    Download  www.bio.ri.ccf.org
    : Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problem...
    Hemant Ishwaran, Udaya B. Kogalur, Xi Chen, Andy J...
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    CSDA
    2008
    84views more  CSDA 2008»
    Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project
    13 years 4 months ago
    Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project
    Download  www.nrs.fs.fed.us
    This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availa...
    Xiaoqian Sun, Zhuoqiong He, John Kabrick
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