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    ETS
    2006
    IEEE
    93views Hardware» more  ETS 2006»
    Enhancing User Support in Open Problem Solving Environments through Bayesian Network Inference Techniques
    13 years 5 months ago
    Enhancing User Support in Open Problem Solving Environments through Bayesian Network Inference Techniques
    Download  www.ifets.info
    Nikolaos K. Tselios, Adrian G. Stoica, Manolis Mar...
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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 5 months ago
    Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
    Download  www.biomedcentral.com
    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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    ECCB
    2003
    IEEE
    183views Computational Biology» more  ECCB 2003»
    Gene networks inference using dynamic Bayesian networks
    13 years 10 months ago
    Gene networks inference using dynamic Bayesian networks
    Download  bioinformatics.oxfordjournals.org
    This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactio...
    Bruno-Edouard Perrin, Liva Ralaivola, Aurél...
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    ICDM
    2009
    IEEE
    141views Data Mining» more  ICDM 2009»
    Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis
    14 years 4 days ago
    Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis
    Download  research.microsoft.com
    —Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-compute...
    Debprakash Patnaik, Srivatsan Laxman, Naren Ramakr...
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    NIPS
    2003
    146views Information Technology» more  NIPS 2003»
    On the Concentration of Expectation and Approximate Inference in Layered Networks
    13 years 6 months ago
    On the Concentration of Expectation and Approximate Inference in Layered Networks
    Download  www.cs.berkeley.edu
    We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This frame...
    XuanLong Nguyen, Michael I. Jordan
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