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    BMCBI
    2007
    139views more  BMCBI 2007»
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
    13 years 7 months ago
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
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    Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathwa...
    Andrew S. Peek
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    BMCBI
    2010
    88views more  BMCBI 2010»
    Proteome scanning to predict PDZ domain interactions using support vector machines
    13 years 7 months ago
    Proteome scanning to predict PDZ domain interactions using support vector machines
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    Background: PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Tw...
    Shirley Hui, Gary D. Bader
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    BMCBI
    2006
    201views more  BMCBI 2006»
    Gene selection algorithms for microarray data based on least squares support vector machine
    13 years 7 months ago
    Gene selection algorithms for microarray data based on least squares support vector machine
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    Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
    E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
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    TITB
    2008
    102views more  TITB 2008»
    Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kern
    13 years 7 months ago
    Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kern
    Download  hwanjoyu.org
    Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their hig...
    Baek Hwan Cho, Hwanjo Yu, Jong Shill Lee, Young Jo...
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    BMCBI
    2010
    151views more  BMCBI 2010»
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
    13 years 7 months ago
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
    Download  www.biomedcentral.com
    Background: Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their ...
    Zhanchao Li, Xuan Zhou, Zong Dai, Xiaoyong Zou
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