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Updated: Jul 9, 2026
09:40
Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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
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.
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.
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