HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features
HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features
复制标题
HNet-DNN：基于异构网络特征，利用深度神经网络推断新的药物与疾病的关联
DOI：
10.1021/acs.jcim.9b01008
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发表时间：
2020-04-27
影响因子：
5.6
通讯作者：
Zhou, Shuigeng
中科院分区：
文献类型：
--
作者：
Liu, Hui;Zhang, Wenhao;Zhou, Shuigeng
药物研发是一项耗时、高成本的工作，迫切需要确定已批准药物的新适应症，即药物重新定位，为药物发现提供了一种经济、高效的途径。随着高通量技术产生的大规模化学、基因组和药理学数据集数量的增加，开发系统合理的计算方法来识别已批准药物的新适应症至关重要。在本文中，我们介绍了HNet-DNN，它利用深度神经网络（DNN），根据从药物-疾病异构网络中提取的特征来预测新的药物-疾病关联。我们使用药物和疾病的这些原始特征来构建药物-药物相似性网络和疾病-疾病相似性网络，而不是直接串联化学和表型特征作为DNN的输入，然后通过整合已知的药物-疾病关联来构建药物-疾病异质网络。随后，我们从异构网络中提取药物-疾病关联的拓扑特征，并使用它们来训练DNN模型。我们密集的性能评估表明，HNet-DNN有效地利用了异构网络的特征，以提高药物-疾病关联的预测性能。与几个典型的分类器和竞争的方法相比，我们的方法不仅取得了最先进的性能，但也有效地缓解了过拟合问题。此外，我们运行HNet-DNN来预测新的药物-疾病关联，并进行案例研究来验证我们方法的有效性。
Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volume of large-scale chemical, genomic and pharmacological data sets generated by high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduced HNet-DNN, which utilizes a deep neural network (DNN) to predict new drug-disease associations based on the features extracted from the drug-disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug-drug similarity network and a disease-disease similarity network, and then built a drug-disease heterogeneous network by integrating known drug-disease associations. Subsequently, we extracted topological features for drug-disease associations from the heterogeneous network, and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug-disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance, but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug-disease associations and carried out case studies to verify the effectiveness of our method.