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    DSS
    2007
    127views more  DSS 2007»
    Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
    14 years 10 months ago
    Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
    Download  www.ischool.drexel.edu
    We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified informationtheory-based Bayesian network algorithm and a modified asso...
    Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsi...
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    IADIS
    2008
    139views Internet Technology» more  IADIS 2008»
    Sisa: Seeded Iterative Signature Algorithm for Biclustering Gene Expression Data
    14 years 11 months ago
    Sisa: Seeded Iterative Signature Algorithm for Biclustering Gene Expression Data
    Download  www.iadis.net
    One approach to reduce the complexity of the task in the analysis of large scale genome-wide expression is to group the genes showing similar expression patterns into what are cal...
    Neelima Gupta, Seema Aggarwal
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    HIS
    2008
    157views Information Technology» more  HIS 2008»
    Bio-Inspired Parameter Tunning of MLP Networks for Gene Expression Analysis
    14 years 11 months ago
    Bio-Inspired Parameter Tunning of MLP Networks for Gene Expression Analysis
    Download  www.liaad.up.pt
    The performance of Artificial Neural Networks is largely influenced by the value of their parameters. Among these free parameters, one can mention those related with the network a...
    André L. D. Rossi, André C. P. L. F....
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    CCGRID
    2005
    IEEE
    84views Distributed And Parallel Com...» more  CCGRID 2005»
    Secret sequence comparison on public grid computing resources
    15 years 3 months ago
    Secret sequence comparison on public grid computing resources
    Download  hal.archives-ouvertes.fr
    Once a new gene has been sequenced, it must be verified whether or not it is similar to previously sequenced genes. In many cases, the organization that sequenced a potentially n...
    Ken-ichi Kurata, Hiroshi Nakamura, Vincent Breton
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    IJCNN
    2006
    IEEE
    121views Neural Networks» more  IJCNN 2006»
    Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions
    15 years 4 months ago
    Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions
    Download  www3.ntu.edu.sg
    Feng Chu, Lipo Wang
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