Phylogeny-agnostic machine learning predicts strain-level phage–host interactions from genomes (Nature Microbiology)

A Nature Microbiology study from Lawrence Berkeley National Laboratory, UC Berkeley and Penn State presents a phylogeny-agnostic machine-learning framework that predicts strain-level phage–host interactions across diverse bacterial genera from genome sequences alone, matching species-specific methods (AUROC 0.67–0.94) across six datasets (115,037 interactions, 949 bacterial strains, 518 phages). Experimental validation of 1,240 predicted E. coli phage–host interactions gave an AUROC of 0.84, and model-guided cocktail design reached up to 97.5% bacterial coverage with five phages.