Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, Hunan, China.
School of Intelligent Manufacturing, Hunan First Normal University, Changsha, 410205, Hunan, China.
BMC Genomics. 2024 Jun 11;25(1):584. doi: 10.1186/s12864-024-10499-5.
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.
药物、靶点和疾病之间关系的合理建模对于药物重定位至关重要。虽然在研究二元关系方面已经取得了重大进展，但需要进一步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位中的应用正在增加，但需要进一步研究确定适用于三元关系的建模方法以及如何捕捉它们复杂的多特征结构。
本研究旨在构建药物、靶点和疾病之间的关系。为了表示这些实体之间的复杂关系，我们使用了异质图结构。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，从而更深入地探索关系。实验结果表明，DTD-GNN 模型在 AUC、精度和 F1 分数方面优于其他图神经网络模型。该研究对于全面了解药物与疾病之间的关系以及进一步研究和应用探索药物-疾病相互作用的机制具有重要意义。该研究揭示了这些关系，为医学中的创新治疗策略提供了可能性。
