The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China.
University of Chinese Academy of Sciences, Beijing, China.
Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbae067.
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.
草药在疾病治疗中的应用已经通过数千年的经验得到了验证。由于多靶标、多成分（MTMC）植物疗法固有的复杂机制，对草药-疾病关联（HDA）的理解还远远不够。大多数现有的预测模型都未能纳入 MTMC 机制。为了解决这个问题，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成分和目标蛋白投射到低维潜在空间中，通过保留其原始特征空间中的相似特征来获得它们的嵌入。为了模拟草药与其成分之间的高阶关系，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，用于模拟疾病和目标蛋白之间的高阶关系。然后通过我们的双通道网络聚合药物和疾病的嵌入，以获得具有评分函数的预测结果。为了评估 HGHDA 的性能，我们在两个基准数据集上进行了一系列广泛的实验，结果表明 HGHDA 优于为 HDA 预测提出的最先进算法。此外，我们对川芎和黄芪的案例研究是验证 HGHDA 有效性的一个有力指标，因为 HGHDA 分别预测川芎和黄芪的前 10 种疾病中有七种和八种已在文献中报道。
