College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
Centre for Computational Biology, School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
BMC Bioinformatics. 2021 Sep 3;22(1):418. doi: 10.1186/s12859-021-04327-w.
Prediction of the drug-target interaction (DTI) is a critical step in the drug repurposing process, which can effectively reduce the following workload for experimental verification of potential drugs' properties. In recent studies, many machine-learning-based methods have been proposed to discover unknown interactions between drugs and protein targets. A recent trend is to use graph-based machine learning, e.g., graph embedding to extract features from drug-target networks and then predict new drug-target interactions. However, most of the graph embedding methods are not specifically designed for DTI predictions; thus, it is difficult for these methods to fully utilize the heterogeneous information of drugs and targets (e.g., the respective vertex features of drugs and targets and path-based interactive features between drugs and targets).
We propose a DTI prediction method DTI-HeNE (DTI based on Heterogeneous Network Embedding), which is specifically designed to cope with the bipartite DTI relations for generating high-quality embeddings of drug-target pairs. This method splits a heterogeneous DTI network into a bipartite DTI network, multiple drug homogeneous networks and target homogeneous networks, and extracts features from these sub-networks separately to better utilize the characteristics of bipartite DTI relations as well as the auxiliary similarity information related to drugs and targets. The features extracted from each sub-network are integrated using pathway information between these sub-networks to acquire new features, i.e., embedding vectors of drug-target pairs. Finally, these features are fed into a random forest (RF) model to predict novel DTIs.
Our experimental results show that, the proposed DTI network embedding method can learn higher-quality features of heterogeneous drug-target interaction networks for novel DTIs discovery.
药物-靶标相互作用（DTI）的预测是药物再利用过程中的关键步骤，可有效减少后续对潜在药物性质进行实验验证的工作量。在最近的研究中，提出了许多基于机器学习的方法来发现药物和蛋白质靶标之间未知的相互作用。最近的一个趋势是使用基于图的机器学习，例如图嵌入，从药物-靶标网络中提取特征，然后预测新的药物-靶标相互作用。然而，大多数图嵌入方法并不是专门为 DTI 预测设计的；因此，这些方法很难充分利用药物和靶标（例如，药物和靶标的各自顶点特征以及药物和靶标之间基于路径的交互特征）的异质信息。
我们提出了一种 DTI 预测方法 DTI-HeNE（基于异构网络嵌入的 DTI），该方法专门用于处理二部 DTI 关系，以生成高质量的药物-靶对嵌入。该方法将异质 DTI 网络拆分为二部 DTI 网络、多个药物同质网络和目标同质网络，并分别从这些子网中提取特征，以更好地利用二部 DTI 关系的特征以及与药物和靶标相关的辅助相似性信息。从每个子网中提取的特征通过这些子网之间的路径信息进行集成，以获取新的特征，即药物-靶对的嵌入向量。最后，将这些特征输入随机森林（RF）模型中以预测新的 DTI。
我们的实验结果表明，所提出的 DTI 网络嵌入方法可以学习更优质的药物-靶标异质相互作用网络特征，用于发现新的 DTI。
