Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases
The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural ne...
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Published in | BMC genomics Vol. 25; no. 1; p. 584 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
England
BioMed Central Ltd
11.06.2024
BioMed Central BMC |
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Abstract | The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.
The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. |
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AbstractList | Motivation The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. Motivation The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. Results The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. Keywords: Drug repurposing, Drug-Target-Disease ternary relationship, DTD-GNN model The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. MotivationThe rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.ResultsThe aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. Abstract Motivation The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure. Results The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine. |
Audience | Academic |
Author | Yang, Mengyun Li, Wenjun Tang, Xiwei Ma, Wanjun |
Author_xml | – sequence: 1 givenname: Wenjun surname: Li fullname: Li, Wenjun organization: Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, Hunan, China – sequence: 2 givenname: Wanjun surname: Ma fullname: Ma, Wanjun organization: Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, Hunan, China – sequence: 3 givenname: Mengyun surname: Yang fullname: Yang, Mengyun organization: School of Intelligent Manufacturing, Hunan First Normal University, Changsha, 410205, Hunan, China – sequence: 4 givenname: Xiwei surname: Tang fullname: Tang, Xiwei email: nudt_xiwei@126.com organization: School of Intelligent Manufacturing, Hunan First Normal University, Changsha, 410205, Hunan, China. nudt_xiwei@126.com |
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Snippet | The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in... Motivation The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities,... Motivation The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been... MotivationThe rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been... MOTIVATIONThe rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been... Abstract Motivation The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress... |
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SubjectTerms | Algorithms Artificial neural networks Associations Computational Biology - methods Disease Diseases Drug Repositioning - methods Drug repurposing Drug-Target-Disease ternary relationship Drugs DTD-GNN model Graph neural networks Graphs Humans Medical innovations Medical research Medicine, Experimental Methods Modelling Neural networks Neural Networks, Computer Pharmaceutical research Regulatory approval Social networks Technology application |
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Title | Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases |
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