School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.
Department of Computer Science and Technology, Shantou University, Shantou 515063, China.
Bioinformatics. 2024 Sep 2;40(9). doi: 10.1093/bioinformatics/btae562.
The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively.
We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs.
Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.
人体中的微生物在影响药物功能方面起着至关重要的作用，因为它们可以调节药物的活性和毒性。目前大多数预测药物-微生物关联的方法都是基于图学习的。然而，多种药物和微生物之间的关系是复杂的、多样化的和异构的。现有的方法往往无法充分地建模这些关系。此外，药物-微生物对的属性表现出长距离的空间相关性，这是之前的方法尚未有效整合的。
我们提出了一种新的预测方法，名为 DHDMP，旨在对多种药物和微生物之间的关系进行编码，并整合沿各种邻接点的属性以及成对的长距离相关性。首先，我们构建了一个具有动态拓扑的超图，其中每个超边表示多个药物节点和微生物节点之间的特定关系。考虑到不同类别节点属性的异质性，我们开发了一种节点类别敏感的超图卷积网络来编码这些多样化的关系。其次，我们分别构建了药物和微生物的同构图，以及药物-微生物异构图，以促进整合每个目标节点的同质和异质邻居的特征。第三，我们引入了一个具有跨图特征传播能力的图卷积网络，将节点特征从同构图传递到异构图，以增强邻居特征表示学习。传播策略有助于两种类型的邻居的特征深度融合。最后，我们设计了空间交叉注意力来编码药物-微生物对的属性，揭示了多个成对属性补丁之间的长距离相关性。综合对比实验表明，我们的方法在药物-微生物关联预测方面优于最新方法。消融研究证明了节点类别敏感的超图卷积网络、具有跨图特征传播的图卷积网络和空间交叉注意力的有效性。对三种药物的案例研究进一步表明，DHDMP 有潜力用于发现感兴趣药物的可靠候选微生物。
源代码和补充材料可在 https://github.com/pingxuan-hlju/DHDMP 上获得。
