对不断变化的知识进行一致的可视化。

Hannah J Tipney, Ronald P Schuyler, Lawrence Hunter
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引用次数: 0

摘要

在生物学中,网络越来越多地用于以简单的符号形式表示复杂的数据。然而,随着生物知识的不断发展,代表这些知识的网络也必须不断发展。由于研究人员定制他们所接触的网络的亲密方式,捕捉和呈现这种类型的知识随着时间的推移而变化尤其具有挑战性。有效地可视化这些知识是很重要的,因为它创造了对复杂系统的洞察力,并刺激了假设的产生和生物学的发现。在这里,我们强调用户自定义的保留,以及与出处相关的知识的收集和可视化如何支持有效和富有成效的网络探索。我们还提出了Hanalyzer系统的扩展ReOrient,它支持在知识变化的情况下进行网络探索和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Consistent visualizations of changing knowledge.

Consistent visualizations of changing knowledge.

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change.

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