ArticleScientific reports2025
A machine learning approach to predict strain-specific phage-host interactions.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- Comparison and correlation of in vitro and in vivo approaches for determining Pseudomonas aeruginosa bacteriophages activity.BMC microbiology · 2026Article
- Bacteriophage therapy for multidrug-resistant bacterial infections: recent advances, clinical evidence, and translational challenges.Archives of microbiology · 2026Review
- Genomic and biological characterization of lytic phages infecting Pseudomonas syringae associated with almond bacterial blast.Scientific reports · 2026Article
- Bacteriophage Therapy AgainstAntibiotics (Basel, Switzerland) · 2026Review
- Phage Therapy in Plant Disease Management: 110 Years of History, Current Challenges, and Future Trends.Plants (Basel, Switzerland) · 2026Review
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Since these interactions are governed by multiple biological mechanisms, it is not a simple task to predict the outcome of a phage infection, which varies even among strains from the same species. In this study, machine learning-based models capable of predicting the host range of phages from sequencing data were developed. Models were trained using phage-bacteria protein-protein interactions (PPI), predicted from PPI databases, and a host-range dataset obtained from experimental assays with 10 Salmonella enterica and 3 Escherichia coli bacteriophages. The performance of prediction models differed among bacteriophages, ranging from 78 to 92% of accuracy in the case of Salmonella and 84-94% in Escherichia phages, with the highest accuracy (94%) achieved for E. coli phage CBDS-07. Results demonstrated the effectiveness of using PPI as a feature to design ML models for phage-bacteria phenotype prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.