A Bipartite Network Module-Based Project to Predict Pathogen–Host Association

Pathogen–host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen–host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. Experimental results show that the proposed method performs better in predicting pathogen–host association than other methods, and some potential pathogen–host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature.

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