MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Abstract

AbstractClinical studies have shown that microbes are closely related to the occurrence of diseases in the human body. It is beneficial for treating diseases by means of microbes to modulate the activity and toxicity of drugs. Therefore, it is significant in predicting associations between drugs and microbes. Recently, there are several computational models for addressing the issue. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Here we introduce a new model (called MDMD) is proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. Mycobacterium tuberculosis(Mtb) is a specific microbe only predicted by the MDMD. An online platform of the MDMD is available inhttps://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded.Author summaryMicrobes inhabit multiple organs of the human body that consist of bacteria, fungi, and viruses. Extensive research shows that the microbes can adjust the efficacy and toxicity of drugs to treat the disease. The efficient and accurate selection of drug-related microbes is important for drug research and disease treatment. However, screening of drug-related microbes relies on traditional lab experiments that are labor-intensive and costly. With the growth of high-throughput data, the research of drug-related microbes urgently needs a computational method in bioinformatics. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Therefore, we propose a new method (called MDMD) based on the aggregation of the metapath to efficiently and accurately predict potential drug-related microbes within the microbes-disease-drug network.