ArticleBMC medical informatics and decision making2018
Automatic extraction of protein-protein interactions using grammatical relationship graph.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- Development and Validation of an AI-Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Evaluating GPT and BERT models for protein-protein interaction identification in biomedical text.Bioinformatics advances · 2024Article
- Predicting cross-tissue hormone-gene relations using balanced word embeddings.Bioinformatics (Oxford, England) · 2022Article
- PCfun: a hybrid computational framework for systematic characterization of protein complex function.Briefings in bioinformatics · 2022Article
- PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.Database : the journal of biological databases and curation · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Text mining for modeling of protein complexes enhanced by machine learning.Bioinformatics (Oxford, England) · 2021Article
- History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.Frontiers in physiology · 2021Review
- Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.BMC genomics · 2020Article
- Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.Database : the journal of biological databases and curation · 2019Article
- Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017).BMC medical informatics and decision making · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.