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    » GPX: Interactive Mining of Gene Expression Data
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    KDD
    2001
    ACM
    156views Data Mining» more  KDD 2001»
    Classification of genes using probabilistic models of microarray expression profiles
    14 years 8 months ago
    Classification of genes using probabilistic models of microarray expression profiles
    Download  noble.gs.washington.edu
    Paul Pavlidis, Christopher Tang, William Stafford ...
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    BMCBI
    2008
    126views more  BMCBI 2008»
    GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed
    13 years 7 months ago
    GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed
    Download  www.biomedcentral.com
    Background: The amount of gene expression data in the public repositories, such as NCBI Gene Expression Omnibus (GEO) has grown exponentially, and provides a gold mine for bioinfo...
    Rong Chen, Rohan Mallelwar, Ajit Thosar, Shivkumar...
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    BMCBI
    2007
    130views more  BMCBI 2007»
    A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression da
    13 years 7 months ago
    A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression da
    Download  www.biomedcentral.com
    Background: Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, th...
    Reuben Thomas, Carlos J. Paredes, Sanjay Mehrotra,...
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    BIRD
    2007
    Springer
    154views Bioinformatics» more  BIRD 2007»
    Mining Spatial Gene Expression Data for Association Rules
    14 years 1 months ago
    Mining Spatial Gene Expression Data for Association Rules
    Download  www.vanhemert.co.uk
    Abstract. We analyse data from the Edinburgh Mouse Atlas GeneExpression Database (EMAGE) which is a high quality data source for spatio-temporal gene expression patterns. Using a n...
    Jano I. van Hemert, Richard A. Baldock
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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 11 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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