MVPHI: a multi-view learning framework for predicting complex microbial interactions

Bacteriophages (phages) are viruses that infect bacteria. As the natural regulators of microbial communities, it plays a crucial role in microbiome turnover. Predicting phage-bacteria interactions (PBIs), as well as bacteria-bacteria interactions (BBIs) is essential for advancing microbiome research. Given the high cost and risk of wet-lab techniques, computational biology and bioinformatics methods are reasonable alternatives. However, existing approaches suffer from the low predictive accuracy and poor efficiency. In this study, we proposed a multi learning-based model named MVPHI for predicting complex microbial interactions. More specifically, we first construct a heterogeneous multi-attributed microbial network (MAMN) based on pathogenic bacteria and associated phages. Next, MVPHI introduces three different view microbial characteristics for training the model, including the statistical-view, textual-view and topology-view features. Experimental results on seven benchmark datasets indicated that MVPHI achieves superior performance compared with the six variant models and eight baseline algorithms. Moreover, case study and protein docking experiments further demonstrated the robustness and generalization of our model. In conclusion, the proposed MVPHI model has potential ability to predict novel PBIs and BBIs, and can also provide valuable insights for phages screening and bacterial community research.