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

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

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	5767-5770
Number of pages	4
Journal	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
State	Published - Dec 1 2006

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 = dec,

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language = "English (US)",

pages = "5767--5770",

journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",

issn = "1557-170X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA. / Zhu, Yitan; Wang, Zuyi; Feng, Yuanjian et al.
In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 01.12.2006, p. 5767-5770.

Research output: Contribution to journal › Article › peer-review

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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SN - 1557-170X

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JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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