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    BIODATAMINING
    2008
    178views more  BIODATAMINING 2008»
    Clustering-based approaches to SAGE data mining
    14 years 10 months ago
    Clustering-based approaches to SAGE data mining
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    Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical appli...
    Haiying Wang, Huiru Zheng, Francisco Azuaje
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    BMCBI
    2007
    163views more  BMCBI 2007»
    Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA - a Bayesian approac
    14 years 10 months ago
    Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA - a Bayesian approac
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    Background: Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge. Resul...
    Carmen Pin, Mark Reuter
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    BIOINFORMATICS
    2007
    195views more  BIOINFORMATICS 2007»
    Context-dependent clustering for dynamic cellular state modeling of microarray gene expression
    14 years 10 months ago
    Context-dependent clustering for dynamic cellular state modeling of microarray gene expression
    Download  www.stat.ucla.edu
    Motivation: High-throughput expression profiling allows researchers to study gene activities globally. Genes with similar expression profiles are likely to encode proteins that ma...
    Shinsheng Yuan, Ker-Chau Li
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    89
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    BMCBI
    2006
    120views more  BMCBI 2006»
    A factor analysis model for functional genomics
    14 years 10 months ago
    A factor analysis model for functional genomics
    Download  www.infornomics.utoronto.ca
    Background: Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While b...
    Rafal Kustra, Romy Shioda, Mu Zhu
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    79
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    BMCBI
    2007
    173views more  BMCBI 2007»
    Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model
    14 years 10 months ago
    Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model
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    Background: Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series dat...
    Ryoko Morioka, Shigehiko Kanaya, Masami Y. Hirai, ...
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