# Phylogeny-agnostic ML predicts strain-level phage–host interactions

Digest AI · Research · published 2026-09-29T00:00:00Z

Canonical: https://digestai.news/story/phylogeny-agnostic-ml-predicts-strain-level-phagehost-interactions

## Summary

Researchers developed a phylogeny‑agnostic machine‑learning framework that predicts strain‑level phage–host interactions from genome sequences alone. The team evaluated over 13.2 million training runs across six datasets, covering 115,037 interactions, 949 bacterial strains and 518 phages. The workflow optimizes genome representations, feature selection and model choice to handle high dimensionality and class imbalance.

Cross‑validation shows AUROC ranging from 0.67 to 0.94, matching species‑specific methods. Experimental validation of 1,240 predicted E. coli phage–host pairs confirmed an AUROC of 0.84, and RB‑TnSeq screens verified that 68.6 % of identified infection mediators were captured computationally. Model‑guided cocktail design achieved up to 97.5 % bacterial coverage with five phages and a 3.1‑fold improvement over promiscuity‑based single‑phage selection.

The platform enables rational phage‑therapy design and precision microbiome engineering across clinical, agricultural and industrial contexts. By removing phylogenetic constraints, it expands the usable phage bank and reduces experimental burden for clinicians and researchers seeking strain‑specific phage candidates.

## Key points

- 13.2 million training runs on 115,037 interactions across 949 strains and 518 phages.
- Cross‑validation AUROC 0.67–0.94; experimental validation AUROC 0.84, 68.6 % mediators captured.
- Model‑guided cocktails reach 97.5 % coverage with five phages, 3.1‑fold better than promiscuity selection.

## Why it matters

This research offers a scalable, phylogeny‑independent tool for selecting strain‑specific phages, potentially accelerating phage therapy and microbiome engineering while reducing reliance on costly lab screening.

## Sources

1. [Phylogeny-agnostic strain-level prediction of phage–host interactions from genomes using machine learning](https://nature.com/articles/s41564-026-02482-5) (Nature Machine Learning, 2026-09-29, primary source)

## Cite

Digest AI, "Phylogeny-agnostic ML predicts strain-level phage–host interactions", 29 September 2026, https://digestai.news/story/phylogeny-agnostic-ml-predicts-strain-level-phagehost-interactions

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