Predicting phage-host interaction via hyperbolic Poincare graph embedding and large-scale protein language technique.

ABSTRACT: Bacteriophages (phages) are increasingly viewed as a promising alternative for the treatment of antibiotic-resistant bacterial infections. However, the diversity of host ranges complicates the identification of target phages. Existing computational tools often fail to accurately identify phages across different bacterial species. In this study, we present GE-PHI, a machine-learning-based model for predicting phage-host interactions (PHIs) by integrating knowledge graph embedding algorithm with a large-scale protein language model. First, a phage-host heterogeneous association network (PHAN) was constructed that incorporated phage-phage and host-host similarity networks. Then, the multi-relational Poincaré graph embedding (MuRP) was used to extract topological patterns. Additionally, we emp

SUBMITTER: Pan J 

PROVIDER: S-EPMC11761876 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature