Zhu Yitan, Wang Zuyi, Feng Yuanjian, Xuan Jianhua, Miller David J, Hoffman Eric P, Wang Yue
Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5767-70. doi: 10.1109/IEMBS.2006.260031.
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.
对于基因组研究中基因模块发现的关键任务，我们提出了一种基于模型的层次数据聚类和可视化算法——视觉统计数据分析器（VISDA），它有效利用人机交互来改善聚类结果。在诊断树的指导下，我们将VISDA应用于一个包含多种不同表型状况的肌肉萎缩症数据集。然后，我们叠加现有的基因调控和基因功能知识（ Ingenuity通路分析）来分析聚类结果，并为肌肉萎缩症的进一步研究生成新的假设。
