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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 2/2018

Open Access 01-07-2018 | Research

Automatic extraction of protein-protein interactions using grammatical relationship graph

Authors: Kaixian Yu, Pei-Yau Lung, Tingting Zhao, Peixiang Zhao, Yan-Yuan Tseng, Jinfeng Zhang

Published in: BMC Medical Informatics and Decision Making | Special Issue 2/2018

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Abstract

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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Metadata
Title
Automatic extraction of protein-protein interactions using grammatical relationship graph
Authors
Kaixian Yu
Pei-Yau Lung
Tingting Zhao
Peixiang Zhao
Yan-Yuan Tseng
Jinfeng Zhang
Publication date
01-07-2018
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-018-0628-4

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