Automatic extraction of protein-protein interactions using grammatical relationship graph.
Automatic extraction of protein-protein interactions using grammatical relationship graph.
复制标题
使用语法关系图自动提取蛋白质 - 蛋白质相互作用。
DOI：
10.1186/s12911-018-0628-4
复制
发表时间：
2018-07-23
影响因子：
3.5
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Yu K;Lung PY;Zhao T;Zhao P;Tseng YY;Zhang J
生物实体(基因、蛋白质、疾病等)之间的关系构成了我们知识的重要组成部分。这些信息大部分是以不同形式的非结构化文本记录的，例如书籍、文章和在线页面。自动提取这些信息并将其以结构化形式存储可以帮助研究人员更容易地获取这些信息，还可以将其纳入高级综合分析。在这项研究中，我们开发了一种新的方法来提取生物实体关系信息，使用自然语言处理(NLP)和图论算法。我们的方法称为GRGT(Gramical Relationship Graph For Triplets)，它不仅提取具有一定关系的术语对，而且提取关系的类型(描述关系的词)。此外，还可以提取关系的方向性。我们的方法是基于一对相互作用存在三元组的假设。三元组被定义为两个词(实体)和一个描述句子中这两个词之间关系的交互词。我们首先使用句子分析工具来获得表示为依存关系图的句子结构，其中单词是节点，边是类型的依存关系。然后提取三元组中词对之间的最短路径，这构成了我们的信息提取方法的基础。然后使用灵活的模式匹配方案将未知关系的三元组图与数据库中带有标签(True或False)的三元组图进行匹配。我们在三个基准数据集上应用该方法来提取蛋白质-蛋白质-蛋白质相互作用(PPI)，并获得了比文献中执行得最好的方法更好的精度。我们开发了一种从生物医学文献中提取蛋白质-蛋白质相互作用的方法。在其他方法中，我们的方法提取的PPI具有较高的精度，这表明我们的方法可以有效地提取PPI并将其存储到数据库中。除了提取PPI外，我们的方法还可以很容易地扩展到提取其他生物实体之间的关系信息。
Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm. Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database. We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature. We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.
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