Multi-view Feature Fusion and Contrastive Learning for Microbe-disease Association Prediction

The relationship between microbes and human health is crucial for understanding pathogenesis and advancing clinical drug development. Investigating the microbe and disease associations(MDA) is essential in this regard. Computational methods offer a cost-effective and efficient approach to detect interactions among biological entities. We propose GCLMDA, a model that utilizes Graph Contrastive Learning for MDA prediction. Firstly, we gather data from diverse and heterogeneous sources to construct similarity networks for microbes and diseases respectively. Moreover, the graph convolution networks help us to learn multi-view node embeddings which are further refined through contrastive learning. Finally, the interacted features of nodes are input into a binary classifier to predict potential MDAs. Through extensive experiments on two public datasets, the proposed model consistently outperforms baseline methods, achieving the highest AUC and AUPR scores of 0.9569 and 0.9595, respectively. The case study on Type II Diabetes provides further evidence of the model effectiveness.