背景:进行传统的湿法实验以指导药物开发是昂贵的,耗时和风险的过程。分析药物功能和重新定位在确定已批准药物的新治疗潜力和发现未治疗疾病的治疗方法中起着关键作用。探索药物-疾病关联对于确定疾病的发病机制和治疗具有深远的意义。然而,通过传统方法可靠地检测药物-疾病关系是昂贵且缓慢的。因此,目前需要研究预测药物-疾病关联的计算方法。
结果:本文提出了一种新的药物-疾病关联预测方法,RAFGAE.首先,RAFGAE将疾病和药物之间的已知关联整合到一个双向网络中。第二,RAFGAE设计Re_GAT框架,其中包括多层图注意网络(GAT)和两个残差网络。多层GAT用于学习节点嵌入,这是通过聚合来自多跳邻居的信息来实现的。两个残差网络用于缓解深度网络过平滑问题,引入了一种注意力机制来组合来自不同注意力层的节点嵌入。第三,构造了两个具有协作训练的图形自编码器(GAE)来模拟标签传播以预测潜在的关联。在此基础上,引入了免费多尺度对抗训练(FMAT)。FMAT通过小梯度对抗扰动迭代增强节点特征质量,提高预测性能。最后,对两个基准数据集的十倍交叉验证表明,RAFGAE优于当前方法。此外,个案研究证实RAFGAE可以检测新的药物-疾病关联.
结论:综合实验结果验证了RAFGAE的实用性和准确性。我们认为这种方法可以作为识别未观察到的疾病-药物关联的极好预测指标。
BACKGROUND: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed.
RESULTS: This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations.
CONCLUSIONS: The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.