Concept recognition for extracting protein interaction relations from biomedical text.
Concept recognition for extracting protein interaction relations from biomedical text.
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DOI：
10.1186/gb-2008-9-s2-s9
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发表时间：
2008
期刊：
影响因子：
12.3
通讯作者：
Hunter L
中科院分区：
文献类型：
--
作者：
Baumgartner WA Jr;Lu Z;Johnson HL;Caporaso JG;Paquette J;Lindemann A;White EK;Medvedeva O;Cohen KB;Hunter L
可靠的信息提取应用程序一直是生物医学文本挖掘界长期追求的目标，如果实现这一目标，将为临床生物学家提供有价值的工具，帮助他们完成日益艰巨的任务，即吸收生物医学文献中包含的知识。我们提出了一种生物医学文本中概念识别的综合方法。概念识别提供了以前的生物医学信息提取工作中很大程度上缺失的关键信息，即与明确定义的知识资源的直接链接，这些资源明确地巩固了概念的语义。本期特刊中讨论的 BioCreative II 任务提供了一个独特的机会来展示生物医学语言处理领域概念识别的有效性。通过蛋白质相互作用关系提取系统的模块化构建，我们提出了生物医学文本中概念识别的几个用例，并将这些用例与实验室生物学家的潜在用途联系起来。当前的信息提取技术正在接近性能标准，概念识别可以开始向实验室生物学家提供高质量的数据。我们的系统作为 BioCreative Meta-Server 项目的一部分并在互联网上提供。
Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet .
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评估生物学的文本挖掘系统：第二次生物综合社区挑战的概述。
影响因子：
12.3
作者：
Krallinger M;Morgan A;Smith L;Leitner F;Tanabe L;Wilbur J;Hirschman L;Valencia A
通讯作者：
Valencia A
影响因子：
5.8
作者：
Chen, LF;Liu, HF;Friedman, C
通讯作者：
Friedman, C
重构语料库：一项可行性研究。
DOI：
10.1186/1747-5333-2-4
发表时间：
2007-09-13
期刊：
Journal of biomedical discovery and collaboration
影响因子：
--
作者：
Johnson HL;Baumgartner WA Jr;Krallinger M;Cohen KB;Hunter L
通讯作者：
Hunter L
DOI：
10.1186/1747-5333-1-3
发表时间：
2006-03-13
期刊：
Journal of biomedical discovery and collaboration
影响因子：
--
作者：
Hersh, William R;Bhupatiraju, Ravi Teja;Ross, Laura;Roberts, Phoebe;Cohen, Aaron M;Kraemer, Dale F
通讯作者：
Kraemer, Dale F
OPENDMAP：开源，本体驱动的概念分析引擎，应用于捕获有关蛋白质转运，蛋白质相互作用和细胞类型特异性基因表达的知识。
影响因子：
3
作者：
Hunter L;Lu Z;Firby J;Baumgartner WA Jr;Johnson HL;Ogren PV;Cohen KB
通讯作者：
Cohen KB