Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.

Yitan Zhu, Zuyi Wang, Yuanjian Feng, Jianhua Xuan, David J. Miller, Eric P. Hoffman, Yue Wang

    Research output: Contribution to journalArticle

    Abstract

    For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

    Fingerprint

    Gene Regulatory Networks
    Genetic Association Studies
    Cluster Analysis
    Muscular Dystrophies
    Genes
    Gene expression
    Visualization
    Research

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

    Cite this

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    title = "Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.",
    abstract = "For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.",
    author = "Yitan Zhu and Zuyi Wang and Yuanjian Feng and Jianhua Xuan and Miller, {David J.} and Hoffman, {Eric P.} and Yue Wang",
    year = "2006",
    month = "12",
    day = "1",
    language = "English (US)",
    pages = "5767--5770",
    journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
    issn = "1557-170X",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - JOUR

    T1 - Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.

    AU - Zhu, Yitan

    AU - Wang, Zuyi

    AU - Feng, Yuanjian

    AU - Xuan, Jianhua

    AU - Miller, David J.

    AU - Hoffman, Eric P.

    AU - Wang, Yue

    PY - 2006/12/1

    Y1 - 2006/12/1

    N2 - For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

    AB - For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

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    M3 - Article

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    EP - 5770

    JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

    JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

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    ER -