BioPortal
BioPortal
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DOI：
10.1007/978-1-4419-1278-7_9
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发表时间：
2009-07-14
期刊：
影响因子：
--
通讯作者：
Yan P
中科院分区：
文献类型：
--
作者：
Chen H;Zeng D;Yan P
BioPortal 项目于 2003 年由亚利桑那大学人工智能实验室及其纽约州卫生部和加州卫生服务部的合作者发起，旨在开发传染病监测系统。该项目由 NSF、DHS、DoD、亚利桑那州卫生服务部和堪萨斯州立大学生物安全中心赞助，并在名为传染病信息学工作委员会 (IDIWC) 的联邦机构间工作组的指导下进行。其合作伙伴包括所有最初的合作者以及美国地质调查局、加州大学戴维斯分校、犹他大学、亚利桑那州卫生服务部、堪萨斯州立大学和国立台湾大学。 BioPortal 系统提供对几种主要传染病数据集的分布式、跨辖区访问，包括肉毒杆菌、西尼罗河病毒、口蹄疫、牲畜综合症。 BioPortal系统架构如图9-1所示。该门户系统提供对各种分布式传染病数据源的基于网络的访问，包括医院急诊科自由文本主诉（英文和中文）以及其他流行病学数据。它具有先进的时空数据分析方法，包括行业标准热点分析算法和内部开发的创新的基于聚类的技术，用于回顾性和前瞻性数据分析。分析结果通过时空可视化工具（STV）显示。 BioPortal 还支持对实验室生成的基因序列信息进行分析和可视化。其社交网络分析模块可用于帮助了解传染病传播过程。
The BioPortal project was initiated in 2003 by the University of Arizona Artificial Intelligence Lab and its collaborators in the New York State Department of Health and the California Department of Health Services to develop an infectious disease surveillance system. The project has been sponsored by NSF, DHS, DoD, Arizona Department of Health Services, and Kansas State University's BioSecurity Center, under the guidance of a federal inter-agency working group named the Infectious Disease Informatics Working Committee (IDIWC). Its partners include all the original collaborators as well as the USGS, University of California, Davis, University of Utah, the Arizona Department of Health Services, Kansas State University, and the National Taiwan University. The BioPortal system provides distributed, cross-jurisdictional access to datasets concerning several major infectious diseases, and including Botulism, West Nile Virus, foot-and-mouth disease, live stock syndromes. Figure 9-1 shows the BioPortal system architecture. This portal system provides Web-based access to a variety of distributed infectious disease data sources including hospital ED free-text chief complaints (both in English and Chinese) as well as other epidemiological data. It features advanced spatial-temporal data analysis methods that include industry standard hotspot analysis algorithms and in-house developed innovative clustering-based techniques for retrospective and prospective data analysis. The analyses results are displayed via Spatio-Temporal Visualizer (STV). BioPortal also supports analysis and visualization of lab-generated gene sequence information. Its social network analysis module can be used to aid in the understanding of infectious disease transmission processes.