A computational model for potential microbe–disease association detection based on improved graph convolutional networks and multi-channel autoencoders

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IntroductionAccumulating evidence shows that human health and disease are closely related to the microbes in the human body.MethodsIn this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe–disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe–disease relationships, disease similarity, and microbial similarity. Then, we adopted the improved GCN and the CSAE to extract neighbor relations in the adjacency matrix and novel feature representations in heterogeneous networks. After that, in order to estimate the likelihood of a potential microbe associated with a disease, we integrated these two types of representations to create unique eigenmatrices for diseases and microbes, respectively, and obtained predicted scores for potential microbe–disease associations by calculating the inner product of these two types of eigenmatrices.Results and discussionBased on the baseline databases such as the HMDAD and the Disbiome, intensive experiments were conducted to evaluate the prediction ability of GCANCAE, and the experimental results demonstrated that GCANCAE achieved better performance than state-of-the-art competitive methods under the frameworks of both 2-fold and 5-fold CV. Furthermore, case studies of three categories of common diseases, such as asthma, irritable bowel syndrome (IBS), and type 2 diabetes (T2D), confirmed the efficiency of GCANCAE.

Keywords

• graph attention networks
• sparse autoencoders
• microbe–disease associations
• computational model
• prediction model