ArticleNature communications2024
Prediction of Klebsiella phage-host specificity at the strain level.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evolutionary dynamics and research hotspots of phage applications againstFrontiers in microbiology · 2026Pooled it
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage-host prediction.Briefings in bioinformatics · 2025Pooled it
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes using machine learning.Nature microbiology · 2026Article
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- AI-assisted phage formulation delivered via injectable hydrogels for localized control of implant-associated infections.Bioactive materials · 2026Article
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- Phage therapy: from basic biology to clinical application.Nature reviews. Microbiology · 2026Review
- PhageMind: generalized strain-level phage host range prediction via meta-learning.Bioinformatics (Oxford, England) · 2026Article
- Modular plug-and-play engineering of Klebsiella phages with dual receptor-binding proteins for efficient host range design.Journal of biological engineering · 2026Article
- DepoCatalog: mapping diversity of 129 recombinantly produced Klebsiella phage depolymerases.Nature communications · 2026Article
- Cross-genus phage design through branching domain and conserved peptide interactions.Journal of biological engineering · 2026Article
- Predicting phage-host specificity.Nature reviews. Microbiology · 2026Article
- DposFinder: an interpretable transformer model for predicting phage-derived polysaccharide depolymerases and their host capsular serotypes.Genome medicine · 2026Article
- Role of the C-terminal modules of Klebsiella phage KP32 receptor-binding protein gp38 in protein and phage functionality.PLoS pathogens · 2026Article
- Capsular specificity in temperate phages of Klebsiella pneumoniae is driven by diverse receptor-binding enzymes.PLoS biology · 2026Article
- Genome characterization and receptor-binding protein identification of Klebsiella phage vB_VIPKPNMC05, a member of a novel viral family Pituviridae.Archives of virology · 2026Article
- Antimicrobial Resistance: How Can We Overcome the Problem?Antibiotics (Basel, Switzerland) · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics.
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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.