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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.
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Automatic Extraction of Protein-protein Interactions using Grammatical Relationship Graph
Background: 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. Methods: 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. Results: 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. Conclusions: 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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- Award ID(s):
- 1743142
- PAR ID:
- 10095244
- Date Published:
- Journal Name:
- BMC medical informatics and decision making
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1472-6947
- Page Range / eLocation ID:
- 35-43
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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