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本文引用的文献

1
Content-rich biological network constructed by mining PubMed abstracts.通过挖掘PubMed摘要构建的内容丰富的生物网络。
BMC Bioinformatics. 2004 Oct 8;5:147. doi: 10.1186/1471-2105-5-147.
2
Rutabaga by any other name: extracting biological names.换个名字的芜菁:提取生物名称。
J Biomed Inform. 2002 Aug;35(4):247-59. doi: 10.1016/s1532-0464(03)00014-5.
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Protein structures and information extraction from biological texts: the PASTA system.蛋白质结构与从生物文本中提取信息:PASTA系统
Bioinformatics. 2003 Jan;19(1):135-43. doi: 10.1093/bioinformatics/19.1.135.
4
Creating knowledge repositories from biomedical reports: the MEDSYNDIKATE text mining system.从生物医学报告创建知识存储库:MEDSYNDIKATE文本挖掘系统。
Pac Symp Biocomput. 2002:338-49.
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Genew: the human gene nomenclature database.Genew:人类基因命名数据库。
Nucleic Acids Res. 2002 Jan 1;30(1):169-71. doi: 10.1093/nar/30.1.169.
6
Creating the gene ontology resource: design and implementation.创建基因本体资源:设计与实现
Genome Res. 2001 Aug;11(8):1425-33. doi: 10.1101/gr.180801.

用于生物医学术语查询的网络服务。

A web service for biomedical term look-up.

作者信息

Harkema Henk, Roberts Ian, Gaizauskas Rob, Hepple Mark

机构信息

Department of Computer Science, University of Sheffield, Sheffield, UK.

出版信息

Comp Funct Genomics. 2005;6(1-2):86-93. doi: 10.1002/cfg.459.

DOI:10.1002/cfg.459
PMID:18629294
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2448598/
Abstract

Recent years have seen a huge increase in the amount of biomedical information that is available in electronic format. Consequently, for biomedical researchers wishing to relate their experimental results to relevant data lurking somewhere within this expanding universe of on-line information, the ability to access and navigate biomedical information sources in an efficient manner has become increasingly important. Natural language and text processing techniques can facilitate this task by making the information contained in textual resources such as MEDLINE more readily accessible and amenable to computational processing. Names of biological entities such as genes and proteins provide critical links between different biomedical information sources and researchers' experimental data. Therefore, automatic identification and classification of these terms in text is an essential capability of any natural language processing system aimed at managing the wealth of biomedical information that is available electronically. To support term recognition in the biomedical domain, we have developed Termino, a large-scale terminological resource for text processing applications, which has two main components: first, a database into which very large numbers of terms can be loaded from resources such as UMLS, and stored together with various kinds of relevant information; second, a finite state recognizer, for fast and efficient identification and mark-up of terms within text. Since many biomedical applications require this functionality, we have made Termino available to the community as a web service, which allows for its integration into larger applications as a remotely located component, accessed through a standardized interface over the web.

摘要

近年来,以电子形式提供的生物医学信息量大幅增加。因此,对于希望将其实验结果与隐藏在这个不断扩展的在线信息世界中的相关数据联系起来的生物医学研究人员来说,以高效方式访问和浏览生物医学信息源的能力变得越来越重要。自然语言和文本处理技术可以通过使诸如MEDLINE等文本资源中包含的信息更容易获取并便于进行计算处理,来促进这项任务。基因和蛋白质等生物实体的名称在不同的生物医学信息源和研究人员的实验数据之间提供了关键联系。因此,在文本中自动识别和分类这些术语是任何旨在管理以电子形式提供的大量生物医学信息的自然语言处理系统的一项基本功能。为了支持生物医学领域的术语识别,我们开发了Termino,这是一个用于文本处理应用程序的大规模术语资源,它有两个主要组件:第一,一个数据库,可以从诸如UMLS等资源中加载大量术语,并与各种相关信息一起存储;第二,一个有限状态识别器,用于快速有效地识别和标记文本中的术语。由于许多生物医学应用都需要此功能,我们已将Termino作为一项网络服务提供给社区,该服务允许将其作为远程组件集成到更大的应用程序中,通过标准化接口在网络上进行访问。