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SimplevisGrid:用于可视化各种生物医学知识和分子系统数据的网格服务。

SimplevisGrid: grid services for visualization of diverse biomedical knowledge and molecular systems data.

作者信息

Stokes Todd H, Wang May D

机构信息

Electrical and Computer Engineering Department, Georgia Institute of Technology, Atlanta, GA 30332, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4178-81. doi: 10.1109/IEMBS.2009.5333932.

Abstract

Biomedical data visualization is a great challenge due to the scale, complexity, and diversity of systems, system component interactions and experimental data. Standards for interoperable data are a good start to addressing these problems, but standardization of visualization technologies is an emerging topic. SimpleVisGrid builds on Cancer Biomedical Informatics Grid (caBIG) common infrastructure for cancer research, and clearly specifies and extends three standard data formats for inputs and outputs to grid services: comma-separated values (CSV), Portable Network Graphics (PNG), and Scalable Vector Graphics (SVG). Four prototype visualizations are available: 2D array data quality visualization, correlation heatmaps between high-dimensional data and associated meta-data, feature landscapes, and biochemical or semantic network graphs. The services and data model are prepared for submission for caBIG Silver-level compatibility review and for integration into automated research workflows. Making these tools available to caBIG developers and ultimately to biomedical researchers can (1) help with biomedical communication, discovery, and decision-making, (2) encourage more research on standardization of visualization formats, and (3) improve the efficiency of large data transfers across the grid.

摘要

由于系统、系统组件交互以及实验数据的规模、复杂性和多样性,生物医学数据可视化是一项巨大挑战。可互操作数据的标准是解决这些问题的良好开端,但可视化技术的标准化仍是一个新兴话题。SimpleVisGrid基于癌症生物医学信息网格(caBIG)用于癌症研究的通用基础设施构建,并明确指定和扩展了三种用于网格服务输入和输出的标准数据格式:逗号分隔值(CSV)、便携式网络图形(PNG)和可缩放矢量图形(SVG)。提供了四种原型可视化:二维数组数据质量可视化、高维数据与相关元数据之间的相关热图、特征景观以及生化或语义网络图。这些服务和数据模型已准备好提交进行caBIG银级兼容性审查,并集成到自动化研究工作流程中。向caBIG开发者以及最终向生物医学研究人员提供这些工具可以:(1)有助于生物医学交流、发现和决策;(2)鼓励对可视化格式标准化进行更多研究;(3)提高跨网格的大数据传输效率。

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