PDF(Pubmed)
Abstract：
Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.
摘要：
草药在疾病治疗中的适用性已经通过数千年的经验得到了验证。由于多靶标和多组分（MTMC）植物疗法固有的复杂机制，对草药疾病关联（HDA）的理解还很不完整。大多数现有的预测模型都无法结合MTMC机制。为了克服这个问题，我们提出了一种新的双通道超图卷积网络，即HGHDA，用于HDA预测。从技术上讲,HGHDA首先采用自动编码器将组分和目标蛋白质投影到低维潜在空间上，以便通过在其原始特征空间中保留相似性特征来获得它们的嵌入。为了对草药及其成分之间的高阶关系进行建模，我们在HGHDA中设计了一个通道来编码一个超图，该超图通过超图卷积描述草药-成分关系的高阶模式。HGHDA中的另一个通道也以相同的方式建立，以模拟疾病和靶蛋白之间的高阶关系。然后通过我们的双通道网络将药物和疾病的嵌入进行汇总，以获得带有评分函数的预测结果。为了评估HGHDA的性能，已经对两个基准数据集进行了一系列广泛的实验，结果表明，HGHDA优于为HDA预测提出的最先进的算法。此外,我们对川雄和黄芪的案例研究是验证HGHDA有效性的有力指标，作为HGHDA预测的川雄和黄芪十大疾病中的七种和八种，分别,已在文献中报道。
