Department of Computer Science, University of Tokyo, Tokyo, Japan.
Bioinformatics. 2010 Jun 15;26(12):i374-81. doi: 10.1093/bioinformatics/btq221.
Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations.
代谢和信号通路是系统生物学中组织知识的一个日益重要的部分。它们用于整合文献中分散的事实的综合解释。生物学家通过阅读大量文章并将其解释为一致的网络来构建途径，但目前构建的大多数模型缺乏与这些文章的直接联系。想要查看原始文章的生物学家必须花费大量时间来收集相关文章并确定与途径相关的部分。此外，随着科学文献每周增加数千篇论文，保持模型的相关性需要持续的维护工作。在本文中，我们介绍了一个系统，该系统旨在将途径可视化工具、文本挖掘系统和注释工具集成到一个无缝的环境中。这将使生物学家能够在途径的各个部分和文章的相关部分之间自由移动，并从大型文本库中识别相关论文。该系统名为 PathText，由 Systems Biology Institute、Okinawa Institute of Science and Technology、National Centre for Text Mining（曼彻斯特大学）和东京大学开发，这些地方的生物学家小组正在使用该系统。
