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    » Clustering Genes Using Heterogeneous Data Sources
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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
    14 years 3 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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    BMCBI
    2007
    130views more  BMCBI 2007»
    Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data
    13 years 11 months ago
    Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data
    Download  www.biomedcentral.com
    Background: Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) pr...
    James J. Chen, Huey-miin Hsueh, Robert R. Delongch...
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    BMCBI
    2007
    123views more  BMCBI 2007»
    Robust clustering in high dimensional data using statistical depths
    13 years 11 months ago
    Robust clustering in high dimensional data using statistical depths
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    Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
    Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
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    BIOINFORMATICS
    2007
    137views more  BIOINFORMATICS 2007»
    Annotation-based distance measures for patient subgroup discovery in clinical microarray studies
    13 years 11 months ago
    Annotation-based distance measures for patient subgroup discovery in clinical microarray studies
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    : Background Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering...
    Claudio Lottaz, Joern Toedling, Rainer Spang
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    BMCBI
    2008
    146views more  BMCBI 2008»
    A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data
    13 years 11 months ago
    A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data
    Download  www.biomedcentral.com
    Background: The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a nu...
    Chang Sik Kim, Cheol Soo Bae, Hong Joon Tcha
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