Predicting Microbe-Disease Associations via Multiple Layer Graph Convolutional Network and Attention Mechanism

Recently clinical evidences have confirmed that human diseases are affected by the microbes inhabiting human bodies. Identifying latent microbe-disease associations can provide a deep insight into the pathogenesis of diseases. However, traditional biological experiments are inefficient and expensive to achieve pathogenic microbes for diseases, computational approaches become a new alternative choice. In this work, we introduce a graph neural network method (MLAGCNMDA) with multiple layers of graph convolutional network and attention mechanism to predict potential microbe-disease pairs. In MLAGCNMDA, a heterogeneous network is constructed based on the known microbe-disease associations and multiple similarities between microbes and diseases. Moreover, nodes embedding of the heterogeneous network are learned by a multi-layer graph convolutional network model, in which the attention mechanism is introduced in each graph convolutional layer to distinguish the importance of neighbor nodes. Finally, a bilinear decoder is used to decode the node embedding to reconstruct microbe-disease associations. The experiments show that our method outperforms the baseline methods with reliable average AUCs of 0.945 and 0.946 in the Leave-one-out and 5-fold cross validation assessment framework. Case studies on two diseases, i.e., colorectal carcinoma and liver cirrhosis, further confirm the reliability and effectiveness of our method.