DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

• Main Authors: He, S. (Author), Yue, Y. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Decision trees; drug; drug development; Drug Development; drug interaction; Drug interactions; Drug Interactions; drug repositioning; Drug Repositioning; Drug-target interaction prediction; Drug-target interactions; Embeddings; Experimental verification; Feature fusion; Forecasting; Graph mining; Graphic methods; Heterogeneous information; Heterogeneous network embedding; Heterogeneous networks; Homogeneous network; Interactive features; machine learning; Machine learning; Machine Learning; Network embedding; Pharmaceutical Preparations; Prediction methods; Similarity informations; Tensors
• Online Access: View Fulltext in Publisher

 Background: 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). Results: 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. Conclusions: 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. © 2021, The Author(s).