Suppr超能文献

HCLS 2.0/3.0:使用Web 2.0/3.0的医疗保健与生命科学数据混搭

HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0.

作者信息

Cheung Kei-Hoi, Yip Kevin Y, Townsend Jeffrey P, Scotch Matthew

机构信息

Center for Medical Informatics, Yale University, 300 George Street, Suite 501, New Haven, CT 06511, USA.

出版信息

J Biomed Inform. 2008 Oct;41(5):694-705. doi: 10.1016/j.jbi.2008.04.001. Epub 2008 Apr 11.

Abstract

We describe the potential of current Web 2.0 technologies to achieve data mashup in the health care and life sciences (HCLS) domains, and compare that potential to the nascent trend of performing semantic mashup. After providing an overview of Web 2.0, we demonstrate two scenarios of data mashup, facilitated by the following Web 2.0 tools and sites: Yahoo! Pipes, Dapper, Google Maps and GeoCommons. In the first scenario, we exploited Dapper and Yahoo! Pipes to implement a challenging data integration task in the context of DNA microarray research. In the second scenario, we exploited Yahoo! Pipes, Google Maps, and GeoCommons to create a geographic information system (GIS) interface that allows visualization and integration of diverse categories of public health data, including cancer incidence and pollution prevalence data. Based on these two scenarios, we discuss the strengths and weaknesses of these Web 2.0 mashup technologies. We then describe Semantic Web, the mainstream Web 3.0 technology that enables more powerful data integration over the Web. We discuss the areas of intersection of Web 2.0 and Semantic Web, and describe the potential benefits that can be brought to HCLS research by combining these two sets of technologies.

摘要

我们描述了当前Web 2.0技术在医疗保健和生命科学(HCLS)领域实现数据混搭的潜力,并将该潜力与进行语义混搭的新兴趋势进行比较。在对Web 2.0进行概述之后,我们展示了由以下Web 2.0工具和网站促成的两种数据混搭场景:雅虎管道(Yahoo! Pipes)、达珀(Dapper)、谷歌地图(Google Maps)和地理共享(GeoCommons)。在第一个场景中,我们利用达珀和雅虎管道在DNA微阵列研究的背景下完成了一项具有挑战性的数据集成任务。在第二个场景中,我们利用雅虎管道、谷歌地图和地理共享创建了一个地理信息系统(GIS)界面,该界面允许对包括癌症发病率和污染流行率数据在内的各类公共卫生数据进行可视化和集成。基于这两个场景,我们讨论了这些Web 2.0混搭技术的优缺点。然后,我们描述语义网,这是主流的Web 3.0技术,能够在网络上实现更强大的数据集成。我们讨论了Web 2.0和语义网的交叉领域,并描述了将这两组技术结合起来可以为HCLS研究带来的潜在好处。

相似文献

1
HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0.
J Biomed Inform. 2008 Oct;41(5):694-705. doi: 10.1016/j.jbi.2008.04.001. Epub 2008 Apr 11.
2
Web GIS in practice VI: a demo playlist of geo-mashups for public health neogeographers.
Int J Health Geogr. 2008 Jul 18;7:38. doi: 10.1186/1476-072X-7-38.
4
Using XML technology for the ontology-based semantic integration of life science databases.
IEEE Trans Inf Technol Biomed. 2004 Jun;8(2):154-60. doi: 10.1109/titb.2004.826724.
5
Semantic mashup of biomedical data.
J Biomed Inform. 2008 Oct;41(5):683-6. doi: 10.1016/j.jbi.2008.08.003. Epub 2008 Aug 12.
6
Development of grid-like applications for public health using Web 2.0 mashup techniques.
J Am Med Inform Assoc. 2008 Nov-Dec;15(6):783-6. doi: 10.1197/jamia.M2731. Epub 2008 Aug 28.
7
yOWL: an ontology-driven knowledge base for yeast biologists.
J Biomed Inform. 2008 Oct;41(5):779-89. doi: 10.1016/j.jbi.2008.05.001. Epub 2008 May 11.
8
An XML message broker framework for exchange and integration of microarray data.
Bioinformatics. 2003 Sep 22;19(14):1844-5. doi: 10.1093/bioinformatics/btg246.
9
An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence.
J Biomed Inform. 2008 Oct;41(5):752-65. doi: 10.1016/j.jbi.2008.02.006. Epub 2008 Feb 29.
10
Semi-automatic web service composition for the life sciences using the BioMoby semantic web framework.
J Biomed Inform. 2008 Oct;41(5):837-47. doi: 10.1016/j.jbi.2008.02.005. Epub 2008 Mar 4.

引用本文的文献

3
Gathering and exploring scientific knowledge in pharmacovigilance.
PLoS One. 2013 Dec 11;8(12):e83016. doi: 10.1371/journal.pone.0083016. eCollection 2013.
4
Chapter 1: Biomedical knowledge integration.
PLoS Comput Biol. 2012;8(12):e1002826. doi: 10.1371/journal.pcbi.1002826. Epub 2012 Dec 27.
5
Injury surveillance in low-resource settings using Geospatial and Social Web technologies.
Int J Health Geogr. 2010 May 24;9:25. doi: 10.1186/1476-072X-9-25.
6
A multi-lingual web service for drug side-effect data.
AMIA Annu Symp Proc. 2009 Nov 14;2009:34-8.
7
Dissemination of health information through social networks: twitter and antibiotics.
Am J Infect Control. 2010 Apr;38(3):182-8. doi: 10.1016/j.ajic.2009.11.004.
9
Translational informatics: enabling high-throughput research paradigms.
Physiol Genomics. 2009 Nov 6;39(3):131-40. doi: 10.1152/physiolgenomics.00050.2009. Epub 2009 Sep 8.
10
Towards Web-based representation and processing of health information.
Int J Health Geogr. 2009 Jan 21;8:3. doi: 10.1186/1476-072X-8-3.

本文引用的文献

1
Advancing translational research with the Semantic Web.
BMC Bioinformatics. 2007 May 9;8 Suppl 3(Suppl 3):S2. doi: 10.1186/1471-2105-8-S3-S2.
4
BioWarehouse: a bioinformatics database warehouse toolkit.
BMC Bioinformatics. 2006 Mar 23;7:170. doi: 10.1186/1471-2105-7-170.
5
Mashups mix data into global service.
Nature. 2006 Jan 5;439(7072):6-7. doi: 10.1038/439006a.
6
YeastHub: a semantic web use case for integrating data in the life sciences domain.
Bioinformatics. 2005 Jun;21 Suppl 1:i85-96. doi: 10.1093/bioinformatics/bti1026.
7
Atlas - a data warehouse for integrative bioinformatics.
BMC Bioinformatics. 2005 Feb 21;6:34. doi: 10.1186/1471-2105-6-34.
8
myGrid: personalised bioinformatics on the information grid.
Bioinformatics. 2003;19 Suppl 1:i302-4. doi: 10.1093/bioinformatics/btg1041.
9
ArrayExpress--a public repository for microarray gene expression data at the EBI.
Nucleic Acids Res. 2003 Jan 1;31(1):68-71. doi: 10.1093/nar/gkg091.
10
BioMOBY: an open source biological web services proposal.
Brief Bioinform. 2002 Dec;3(4):331-41. doi: 10.1093/bib/3.4.331.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验