PDF(Pubmed)
Abstract：
BACKGROUND: 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.
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.
摘要：
背景：药物之间关系的合理建模，目标和疾病对于药物重新定位至关重要。虽然在研究二元关系方面取得了重大进展，需要进一步的研究来加深我们对三元关系的理解。图神经网络在药物重定向中的应用越来越多,但是需要进一步的研究来确定三元关系的适当建模方法以及如何捕获它们复杂的多特征结构。
结果：本研究的目的是构建药物之间的关系，目标和疾病。为了表示这些实体之间的复杂关系，我们使用了异构图结构。此外,我们提出了一种DTD-GNN模型，该模型结合了图卷积网络和图注意力网络来学习特征表示和关联信息，促进对关系的更彻底的探索。实验结果表明，DTD-GNN模型在AUC方面优于其他图神经网络模型，Precision,和F1得分。这项研究对于全面了解药物与疾病之间的关系具有重要意义,以及在探索药物-疾病相互作用机制方面的进一步研究和应用。这项研究揭示了这些关系，为医学创新治疗策略提供可能性。
结果：本研究的目的是构建药物之间的关系，目标和疾病。为了表示这些实体之间的复杂关系，我们使用了异构图结构。此外,我们提出了一种DTD-GNN模型，该模型结合了图卷积网络和图注意力网络来学习特征表示和关联信息，促进对关系的更彻底的探索。实验结果表明，DTD-GNN模型在AUC方面优于其他图神经网络模型，Precision,和F1得分。这项研究对于全面了解药物与疾病之间的关系具有重要意义,以及在探索药物-疾病相互作用机制方面的进一步研究和应用。这项研究揭示了这些关系，为医学创新治疗策略提供可能性。
