Extraction of protein-protein interactions using natural language processing based pattern matching
Extraction of protein-protein interactions using natural language processing based pattern matching
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DOI：
10.1109/bibm.2017.8217847
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Kaixian Yu;Tingting Zhao;Peixiang Zhao;Jinfeng Zhang
中科院分区：
文献类型：
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作者：
Kaixian Yu;Tingting Zhao;Peixiang Zhao;Jinfeng Zhang
我们知识的一个重要部分是两个术语之间的关系。然而，这些信息中的大多数以各种形式记录为非结构化文本，如书籍，在线文章和网页。提取这些信息并将其存储在结构化的数据库中，可以帮助人们更方便地利用这些信息。在这项研究中，我们提出了一种新的方法来提取的关系信息的基础上自然语言处理（NLP）和图论算法。我们的方法，三元组语法关系图（GRGT），提取三层信息：有一定关系的术语对，到底是什么类型的关系，这种关系是什么直接。广电计量通过使用自然语言处理对句子进行语法分析而获得的语法图进行工作。利用感兴趣的词之间的最短路径从图中提取模式。我们设计了一个决策树来进行模式匹配。将GRGT应用于生物医学文献中蛋白质-蛋白质相互作用（PPI）的提取，获得了比文献中最好的方法更好的精度。除了提取PPI，我们的方法可以很容易地扩展到提取其他生物实体之间的关系信息。
A significant part of our knowledge is relationships between two terms. However, most of these information is documented as unstructured text in various forms, like books, online articles and webpages. Extract those information and store them in a structured database could help people utilize these information more conveniently. In this study, we proposed a novel approach to extract the relationships information based on Nature Language Processing (NLP) and graph theoretic algorithm. Our method, Grammatical Relationship Graph for Triplets (GRGT), extracts three layers of information: the pairs of terms that have certain relationship, exactly what type of the relationship is, and what direct this relationship is. GRGT works on a grammatical graph obtained by parsed the sentence using Natural Language Processing. Patterns were extracted from the graph by shortest path among the words of interests. We have designed a decision tree to make the pattern matching. GRGT was applied to extract the protein-protein-interactions (PPIs) from biomedical literature, and obtained better precision than the best performing method in literature. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bioentities.