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Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study

BACKGROUND: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is ope...

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Detalles Bibliográficos
Autores principales: Wang, Meng, Wang, Haofen, Liu, Xing, Ma, Xinyu, Wang, Beilun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277366/
https://www.ncbi.nlm.nih.gov/pubmed/34185011
http://dx.doi.org/10.2196/28277
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author Wang, Meng
Wang, Haofen
Liu, Xing
Ma, Xinyu
Wang, Beilun
author_facet Wang, Meng
Wang, Haofen
Liu, Xing
Ma, Xinyu
Wang, Beilun
author_sort Wang, Meng
collection PubMed
description BACKGROUND: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. OBJECTIVE: Leveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity. METHODS: We propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. RESULTS: To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy. CONCLUSIONS: We propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines.
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spelling pubmed-82773662021-07-26 Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study Wang, Meng Wang, Haofen Liu, Xing Ma, Xinyu Wang, Beilun JMIR Med Inform Original Paper BACKGROUND: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. OBJECTIVE: Leveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity. METHODS: We propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. RESULTS: To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy. CONCLUSIONS: We propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines. JMIR Publications 2021-06-24 /pmc/articles/PMC8277366/ /pubmed/34185011 http://dx.doi.org/10.2196/28277 Text en ©Meng Wang, Haofen Wang, Xing Liu, Xinyu Ma, Beilun Wang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.06.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wang, Meng
Wang, Haofen
Liu, Xing
Ma, Xinyu
Wang, Beilun
Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title_full Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title_fullStr Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title_full_unstemmed Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title_short Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
title_sort drug-drug interaction predictions via knowledge graph and text embedding: instrument validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277366/
https://www.ncbi.nlm.nih.gov/pubmed/34185011
http://dx.doi.org/10.2196/28277
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