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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
09:40

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Published on: June 11, 2015

PhageMind: generalized strain-level phage host range prediction via meta-learning.

Yang Shen1, Keming Shi2, Chen Yu3

  • 1Department of Electrical Engineering, City University of Hong Kong, Hong Kong (SAR), China.

Bioinformatics (Oxford, England)
|July 7, 2026
PubMed
Summary

PhageMind accurately predicts bacterial host range for bacteriophages (phages) across different genera, even with limited data. This framework enables efficient transfer of knowledge for scalable phage-host interaction studies.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Bacteriophages (phages) are crucial for regulating bacterial populations and have applications in therapy and industry.
  • Accurate prediction of phage host range at the strain level is essential but challenging due to limitations in current computational methods.
  • Existing methods often lack generalizability across bacterial taxa or require extensive training data, hindering predictions for understudied lineages.

Purpose of the Study:

  • To develop a novel computational framework, PhageMind, for predicting strain-level phage-host interactions.
  • To enable efficient knowledge transfer across bacterial genera, particularly in data-limited scenarios.
  • To create a scalable and adaptable tool for understanding phage-host dynamics in diverse bacterial populations.

Main Methods:

  • PhageMind utilizes a knowledge graph incorporating phage tail fiber proteins and bacterial O-antigen biosynthesis gene clusters to model phage-host relationships.
  • The framework learns shared principles of phage-bacterium interactions from well-characterized systems.
  • It rapidly adapts these principles to new bacterial genera using minimal known interactions.

Main Results:

  • PhageMind demonstrates high prediction accuracy for phage-host interactions across four diverse bacterial genera: Escherichia, Klebsiella, Vibrio, and Alteromonas.
  • The model exhibits strong adaptability to new bacterial lineages, maintaining robust performance in leave-one-genus-out evaluations.
  • PhageMind effectively predicts strain-level interactions even with limited reference data, showcasing its utility in data-scarce environments.

Conclusions:

  • PhageMind offers a scalable and practical solution for predicting phage-host interactions across a wide range of bacterial genera.
  • The framework's ability to leverage knowledge transfer makes it valuable for studying the global phageome.
  • PhageMind has the potential to significantly advance applications such as phage therapy and biocontrol by improving host range prediction accuracy.