Automatic extraction of protein-protein interactions using grammatical relationship graph

Abstract

BackgroundRelationships 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.MethodsOur 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.ResultsWe 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.ConclusionsWe 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.

Validation of a Semiautomated Natural Language Processing–Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance

“…00043 useful information from unstructured narrative text. 4,5 NLP algorithms have been widely applied to medical research in recent years for various tasks, including extracting protein-protein interactions, 6 predicting gene-disease associations from biomedical literature databases, 7 improving the sensitivity of screening for suicide behaviors among pregnant women from electronic health record systems, 8 and correlating mammographic imaging features with pathologic findings. 9 Our group previously used an NLP algorithm to automatically parse breast pathologic reports in both English and Chinese.…”

High-Precision Biomedical Relation Extraction for Reducing Human Curation Efforts in Industrial Applications

“…However, we are mainly interested in the precision metric, motivated by a real-world application of the system. We compared our method to existing methods in literature, including rule-based approaches [19], [20], featureand kernel-based approaches [21]- [26], and neural network approaches [27]- [31]. Additionally, we compared our system to recent transformer-based methods pre-trained on biomedical texts, namely BioBERT [34] and SciBERT [35].…”

“…Fundel et al [19] showed how a small number of carefully designed rules based on the shortest dependency path (SDP) between two examined entities produces fairly good results. Yu et al [20] exploited dependency parse trees and a flexible pattern matching scheme, enriching the system with a decision tree classifier. Diverse syntactic and orthography features have been extensively used in feature-and kernel-based methods.…”

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

“…These sentences were then dependency parsed. A protein-protein extraction system previously developed was then applied based on features extracted during the previous steps [ 28 , 54 , 55 ]. Finally, features were extracted from preceding steps in the pipeline and were used as input for training an XGBoost classifier [ 56 ].…”

“…It was constructed based on the personal knowledge of the authors and manual review of sentences in the literature known to contain protein-protein interactions [ 21 ]. This dictionary has been successfully employed in similar applications [ 7 , 28 , 61 – 64 ].…”