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    » Classification with reject option in gene expression data
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    BMCBI
    2005
    167views more  BMCBI 2005»
    Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes
    14 years 9 months ago
    Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes
    Download  www.biomedcentral.com
    Background: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer...
    Patrick Warnat, Roland Eils, Benedikt Brors
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    BMCBI
    2008
    128views more  BMCBI 2008»
    Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions
    14 years 9 months ago
    Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions
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    Background: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. ...
    Ki-Yeol Kim, Dong Hyuk Ki, Hei-Cheul Jeung, Hyun C...
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    KDD
    2001
    ACM
    156views Data Mining» more  KDD 2001»
    Classification of genes using probabilistic models of microarray expression profiles
    15 years 9 months ago
    Classification of genes using probabilistic models of microarray expression profiles
    Download  noble.gs.washington.edu
    Paul Pavlidis, Christopher Tang, William Stafford ...
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    BMCBI
    2004
    205views more  BMCBI 2004»
    A combinational feature selection and ensemble neural network method for classification of gene expression data
    14 years 9 months ago
    A combinational feature selection and ensemble neural network method for classification of gene expression data
    Download  www.biomedcentral.com
    Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
    Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
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    GECCO
    2007
    Springer
    179views Optimization» more  GECCO 2007»
    Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms
    15 years 3 months ago
    Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms
    Download  www.cs.bham.ac.uk
    Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomark...
    Alper Küçükural, Reyyan Yeniterzi...
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