School of Computer Science and Engineering, Key Lab of Computer Network and Information Integration, MOE, Southeast University, Nanjing, 210018, China.
BMC Bioinformatics. 2020 Dec 9;21(1):566. doi: 10.1186/s12859-020-03898-4.
Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models.
Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators.
Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug-disease associations and give pharmaceutical personnel a new perspective to develop new drugs.
药物重定位是发现已知药物新用途的一种重要且有效的方法。研究人员在药物重定位方面仅限于使用特定类型的协同过滤（CF）模型，例如基于邻域的方法，这些方法擅长挖掘少数强药物-疾病关联中包含的局部信息，或者基于潜在因素的模型，这些模型有效地捕获大多数药物-疾病关联共享的全局信息。很少有研究人员将这两种 CF 模型结合起来，得出一种能够兼具两者优势的混合模型。此外，冷启动问题一直是计算药物重定位领域的主要挑战，限制了相关模型的推理能力。
受记忆网络的启发，我们提出了混合注意记忆网络（HAMN）模型，这是一种将两种 CF 模型以非线性方式结合在一起的深度架构。首先，记忆单元和注意力机制结合在一起，生成一个邻域贡献表示，以捕捉少数强药物-疾病关联的局部结构。然后，使用自动编码器的变体来提取药物和疾病的潜在因素，以捕获大多数药物-疾病关联共享的整体信息。在此过程中，药物和疾病的辅助信息可以帮助缓解冷启动问题。最后，在预测阶段，邻域贡献表示与药物潜在因素和疾病潜在因素相结合，生成预测值。在两个数据集上的综合实验结果表明，我们提出的 HAMN 模型在 AUC、AUPR 和 HR 指标上均优于其他对比模型。
通过两个药物重定位数据集的性能，我们相信 HAMN 模型为提高药物-疾病关联的预测精度提供了一种新的解决方案，并为制药人员提供了开发新药的新视角。
