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    BMCBI
    2006
    158views more  BMCBI 2006»
    Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
    13 years 5 months ago
    Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
    Download  www.biomedcentral.com
    Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
    Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
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    IWANN
    2009
    Springer
    157views Neural Networks» more  IWANN 2009»
    Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints
    14 years 1 days ago
    Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints
    Download  ftp.esat.kuleuven.ac.be
    This work proposes the use of maximal variation analysis for feature selection within least squares support vector machines for survival analysis. Instead of selecting a subset of ...
    Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
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    BMCBI
    2005
    135views more  BMCBI 2005»
    A robust two-way semi-linear model for normalization of cDNA microarray data
    13 years 5 months ago
    A robust two-way semi-linear model for normalization of cDNA microarray data
    Download  www.biomedcentral.com
    Background: Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative e...
    Deli Wang, Jian Huang, Hehuang Xie, Liliana Manzel...
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    BMCBI
    2011
    255views Artificial Intelligence» more  BMCBI 2011»
    A Beta-Mixture Model for Dimensionality Reduction, Sample Classification and Analysis
    12 years 9 months ago
    A Beta-Mixture Model for Dimensionality Reduction, Sample Classification and Analysis
    Download  www.biomedcentral.com
    Background: Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper ...
    Kirsti Laurila, Bodil Oster, Claus L. Andersen, Ph...
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    BMCBI
    2006
    173views more  BMCBI 2006»
    Kernel-based distance metric learning for microarray data classification
    13 years 5 months ago
    Kernel-based distance metric learning for microarray data classification
    Download  www.biomedcentral.com
    Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
    Huilin Xiong, Xue-wen Chen
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