Discovering patterns to extract protein-protein interactions from the literature: Part II
Discovering patterns to extract protein-protein interactions from the literature: Part II
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DOI：
10.1093/bioinformatics/bti493
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发表时间：
2005-08-01
期刊：
影响因子：
5.8
通讯作者：
Li, M
中科院分区：
文献类型：
--
作者：
Hao, Y;Zhu, XY;Li, M
动机：多年来发表的数百万篇研究文章中隐藏了大量的蛋白质-蛋白质相互作用关系，而且这个数字还在不断增长。自动重新发现它们是一项具有挑战性的生物信息学任务。这个问题的解决方案也远远超出了生物信息学的范畴。结果：我们研究了一种新的方法，涉及自动发现英语表达模式，优化它们，并使用它们来提取蛋白质-蛋白质相互作用。在另一篇姊妹论文中，我们描述了如何生成与蛋白质-蛋白质相互作用相关的英语表达模式，仅此一项就已经达到了比其他自动系统高得多的准确率和召回率。本文继续介绍我们的理论，重点是如何改进模式。设计了一种基于最小描述长度(MDL)的模式优化算法，对模式进行约简和合并。这大大提高了泛化能力，从而提高了召回率和查准率，这一点得到了oureximents.com的证实。可用性：http://spies.cs.tsinghua.edu.cnContact：zxy-dcs@tsinghua.edu.cn
Motivation: An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics.Results: We study a new approach that involves automatically discovering English expression patterns, optimizing them and using them to extract protein-protein interactions. In a sister paper, we described how to generate English expression patterns related to protein-protein interactions, and this approach alone has already achieved precision and recall rates significantly higher than those of other automatic systems. This paper continues to present our theory, focusing on how to improve the patterns. A minimum description length (MDL)-based pattern-optimization algorithm is designed to reduce and merge patterns. This has significantly increased generalization power, and hence the recall and precision rates, as confirmed by ourexperiments.Availability: http://spies.cs.tsinghua.edu.cnContact: zxy-dcs@tsinghua.edu.cn