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

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    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.

    Original languageEnglish (US)
    Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
    Pages5767-5770
    Number of pages4
    DOIs
    StatePublished - Dec 1 2006
    Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
    Duration: Aug 30 2006Sep 3 2006

    Publication series

    NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
    ISSN (Print)0589-1019

    Other

    Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
    CountryUnited States
    CityNew York, NY
    Period8/30/069/3/06

    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

    Zhu, Y., Wang, Z., Feng, Y., Xuan, J., Miller, D. J., Hoffman, E. P., & Wang, Y. (2006). Phenotypic-specific gene module discovery using a diagnostic tree and caBIG™ VISDA. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 5767-5770). [4029547] (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260031
    Zhu, Yitan ; Wang, Zuyi ; Feng, Yuanjian ; Xuan, Jianhua ; Miller, David Jonathan ; Hoffman, Eric P. ; Wang, Yue. / Phenotypic-specific gene module discovery using a diagnostic tree and caBIG™ VISDA. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. pp. 5767-5770 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).
    @inproceedings{d763c3bd29174d4e994345355a0cbe0b,
    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 Jonathan} and Hoffman, {Eric P.} and Yue Wang",
    year = "2006",
    month = "12",
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    doi = "10.1109/IEMBS.2006.260031",
    language = "English (US)",
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    Zhu, Y, Wang, Z, Feng, Y, Xuan, J, Miller, DJ, Hoffman, EP & Wang, Y 2006, Phenotypic-specific gene module discovery using a diagnostic tree and caBIG™ VISDA. in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06., 4029547, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp. 5767-5770, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, United States, 8/30/06. https://doi.org/10.1109/IEMBS.2006.260031

    Phenotypic-specific gene module discovery using a diagnostic tree and caBIG™ VISDA. / Zhu, Yitan; Wang, Zuyi; Feng, Yuanjian; Xuan, Jianhua; Miller, David Jonathan; Hoffman, Eric P.; Wang, Yue.

    28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5767-5770 4029547 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    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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    Zhu Y, Wang Z, Feng Y, Xuan J, Miller DJ, Hoffman EP et al. Phenotypic-specific gene module discovery using a diagnostic tree and caBIG™ VISDA. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5767-5770. 4029547. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260031