用图扩散网络提升药物基因关联预测准确率
Graph Diffusion Network for Drug-Gene Prediction
- 基于元路径构建同质图，捕捉药物与基因间相似关系
- 并行扩散网络生成难负样本，提升对比学习效率
- 在DGIdb 4.0数据集上表现优于现有方法
药物-基因关联预测对新药研发和疾病治疗至关重要。尽管图神经网络（GNN）在此任务中表现有效，但仍面临数据稀疏和对比学习负样本获取效率低的问题。本文提出图扩散网络用于药物-基因预测（GDNDGP），通过两项关键创新克服上述挑战：首先，采用基于元路径的同质图学习，捕捉药物-药物和基因-基因间的关联，确保相似实体共享嵌入空间；其次，引入并行扩散网络，在训练过程中生成难负样本，无需耗时的负样本检索。模型在DGIdb 4.0数据集上取得优异性能，并在三元药物-基因-疾病网络中展现出强泛化能力。实验结果表明，该方法在处理复杂异构关系时显著优于现有方法。源代码已公开于https://github.com/csjywu1/GDNDGP。
原文摘要 · Abstract (English)
Predicting drug-gene associations is crucial for drug development and disease treatment. While graph neural networks (GNN) have shown effectiveness in this task, they face challenges with data sparsity and efficient contrastive learning implementation. We introduce a graph diffusion network for drug-gene prediction (GDNDGP), a framework that addresses these limitations through two key innovations. First, it employs meta-path-based homogeneous graph learning to capture drug-drug and gene-gene relationships, ensuring similar entities share embedding spaces. Second, it incorporates a parallel diffusion network that generates hard negative samples during training, eliminating the need for exhaustive negative sample retrieval. Our model achieves superior performance on the DGIdb 4.0 dataset and demonstrates strong generalization capability on tripartite drug-gene-disease networks. Results show significant improvements over existing methods in drug-gene prediction tasks, particularly in handling complex heterogeneous relationships. The source code is publicly available at https://github.com/csjywu1/GDNDGP.
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