ArticleVirus evolution2024
Computational host range prediction-The good, the bad, and the ugly.
Article in Virus evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Comparison and correlation of in vitro and in vivo approaches for determining Pseudomonas aeruginosa bacteriophages activity.BMC microbiology · 2026Article
- Co-migration of hundreds of microbial species over metres drives selection and promotes non-motile hitchhikers.Nature communications · 2026Article
- Review
- Genomic and experimental characterization of unique Antarctic temperate phage Perkons reveals multiple integrases and complex lysogenic dynamics in Psychrobacillus sp. L4.BMC microbiology · 2026Article
- Microbial decomposer diversity and metabolic function during the decomposition of brine shrimp carcasses in a saline lake.Microbiome · 2026Article
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Unexplored diversity and molecular genetic signatures of chimallin and phuz encoding phages.BMC genomics · 2025Article
- Development of Chimera AMP-Endolysin with Wider Spectra Against Gram-Negative Bacteria Using High-Throughput Assay.Viruses · 2025Article
- Identification and classification of the genomes of novel microviruses in poultry slaughterhouse.Frontiers in microbiology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of
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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.