EpiphaNet:支持生物医学发现的交互式工具。

Trevor Cohen, G Kerr Whitfield, Roger W Schvaneveldt, Kavitha Mukund, Thomas Rindflesch
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引用次数: 0

摘要

未标记的:背景。EpiphaNet是一个交互式知识发现系统,它使研究人员能够使用语言处理技术的组合来探索从MEDLINE提取的关系的可视化集。在本文中,我们讨论了该系统的理论和方法基础,并评估了基于文献发现的基础模型的效用。此外,我们提出的结果总结,从定性分析与系统的互动超过六个小时的基础医学科学家。结果:该系统能够模拟开放和封闭的发现,并显示出在相关研究人员的专业领域内产生令人惊讶和有趣的关联。结论:EpiphaNet提供了概念之间关联的交互式可视化表示,这源于MEDLINE中生物医学引用的分布统计数据。该工具可在线使用,为生物医学科学家提供了识别和探索他们感兴趣的关联的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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EpiphaNet: An Interactive Tool to Support Biomedical Discoveries.

Unlabelled: Background. EpiphaNet is an interactive knowledge discovery system which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature-based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists.

Results: The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned.

Conclusions: EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them.

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Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation. The language of discovery. Bias associated with mining electronic health records. Literature-based Resurrection of Neglected Medical Discoveries. A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology.
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