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    BMCBI
    2006
    88views more  BMCBI 2006»
    A two-sample Bayesian t-test for microarray data
    13 years 9 months ago
    A two-sample Bayesian t-test for microarray data
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    Background: Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use ...
    Richard J. Fox, Matthew W. Dimmic
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    BMCBI
    2007
    164views more  BMCBI 2007»
    Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
    13 years 9 months ago
    Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
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    Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
    Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...
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    BMCBI
    2004
    134views more  BMCBI 2004»
    Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
    13 years 9 months ago
    Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
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    Background: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature ...
    Ricardo Z. N. Vêncio, Helena Brentani, Diogo...
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    FUIN
    2008
    108views more  FUIN 2008»
    Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
    13 years 7 months ago
    Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
    Download  lirias.kuleuven.be
    Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
    Wannes Meert, Jan Struyf, Hendrik Blockeel
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    PROMISE
    2010
    464views Software Engineering» more  PROMISE 2010»
    Defect cost flow model: a Bayesian network for predicting defect correction effort
    13 years 4 months ago
    Defect cost flow model: a Bayesian network for predicting defect correction effort
    Download  promisedata.org
    Background. Software defect prediction has been one of the central topics of software engineering. Predicted defect counts have been used mainly to assess software quality and est...
    Thomas Schulz, Lukasz Radlinski, Thomas Gorges, Wo...
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