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    BIOCOMP
    2006
    233views Bioinformatics» more  BIOCOMP 2006»
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    13 years 7 months ago
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    Download  ww1.ucmss.com
    Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
    Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia
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    BMCBI
    2011
    254views Artificial Intelligence» more  BMCBI 2011»
    Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities
    12 years 9 months ago
    Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities
    Download  www.biomedcentral.com
    Background: Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Un...
    Yao Fu, Laura R. Jarboe, Julie A. Dickerson
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    CIBCB
    2007
    IEEE
    204views Computational Biology» more  CIBCB 2007»
    Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression
    13 years 10 months ago
    Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression
    Download  www.jihe.net
    Abstract-- The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ...
    Ji He, Xinbin Dai, Xuechun Zhao
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    IJCNN
    2000
    IEEE
    139views Neural Networks» more  IJCNN 2000»
    Evolving Neural Network Structures Using Axonal Growth Mechanisms
    13 years 10 months ago
    Evolving Neural Network Structures Using Axonal Growth Mechanisms
    Download  www.ifi.uzh.ch
    In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the geno...
    Peter Eggenberger
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    FGCN
    2008
    IEEE
    155views Communications» more  FGCN 2008»
    Modeling the Marginal Distribution of Gene Expression with Mixture Models
    13 years 8 months ago
    Modeling the Marginal Distribution of Gene Expression with Mixture Models
    Download  seq.cbrc.jp
    We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
    Edward Wijaya, Hajime Harada, Paul Horton
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