Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods failed to deeply integrate the neighborhood topological structure and the node attributes of an interested drug-disease node pair. We propose a new prediction method, ANPred, to learn and integrate pairwise attribute information and neighbor topology information from the similarities and associations related to drugs and diseases. First, a bi-layer heterogeneous network with intra-layer and inter-layer connections is established to combine the drug similarities, the disease similarities, and the drug-disease associations. Second, the embedding of a pair of drug and disease is constructed based on integrating multiple biological premises about drugs and diseases. The learning framework based on multi-layer convolutional neural networks is designed to learn the attribute representation of the pair of drug and disease nodes from its embedding. The sequences composed of neighbor nodes are formed based on random walk on the heterogeneous network. A framework based on fully-connected autoencoder and skip-gram module is constructed to learn the neighbor topological representations of nodes. The cross-validation results indicate the performance of ANPred is superior to several state-of-the-art methods. The case studies on 5 drugs further confirm the ability of ANPred in discovering the potential drug-disease association candidates.

中文翻译：

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通过整合成对属性和邻居拓扑结构预测药物相关疾病

确定已批准药物的新疾病适应症有助于降低药物开发的成本和时间。大多数最近的方法都集中在利用与药物和疾病相关的各种信息来预测候选药物-疾病关联。然而，以前的方法未能将邻域拓扑结构和感兴趣的药物-疾病节点对的节点属性深度整合。我们提出了一种新的预测方法 ANPred，从与药物和疾病相关的相似性和关联性中学习和整合成对属性信息和邻居拓扑信息。首先，建立具有层内和层间连接的双层异构网络，以结合药物相似性、疾病相似性和药物-疾病关联。第二，药物和疾病对的嵌入是在整合多个关于药物和疾病的生物学前提的基础上构建的。基于多层卷积神经网络的学习框架旨在从其嵌入中学习药物和疾病节点对的属性表示。由邻居节点组成的序列是基于异构网络上的随机游走形成的。构建了一个基于全连接自编码器和skip-gram模块的框架来学习节点的邻居拓扑表示。交叉验证结果表明 ANPred 的性能优于几种最先进的方法。5 种药物的案例研究进一步证实了 ANPred 发现潜在药物-疾病关联候选者的能力。