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.
authors.affiliation: Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA.
authors.forename: Yitan
authors.initials: Y
authors.lastname: Zhu
authors.forename: Zuyi
authors.initials: Z
authors.lastname: Wang
authors.forename: Yuanjian
authors.initials: Y
authors.lastname: Feng
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: David J
authors.initials: DJ
authors.lastname: Miller
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001706:Biopsy; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D009136:Muscular Dystrophies / Q000235:genetics*; D010363:Pattern Recognition, Automated; D010641:Phenotype; D011336:Probability; D011381:Programming Languages; D012984:Software