ArticleVirus evolution2024
Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.
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 9 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 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
- 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
- A qPCR-Based Screening Platform for Exploratory Assessment of Phage Training Outcomes inViruses · 2026Article
- From structure to design: experimental and AI-driven approaches in receptor-binding protein engineering for reprogramming phage host range.Archives of microbiology · 2026Review
- A machine learning approach to predict strain-specific phage-host interactions.Scientific reports · 2025Article
- Identification of a mouse-virulent recombinant type I/III Toxoplasma gondii strain in liver cytology of an immunosuppressed cat infected with FeLV-C subgroup.Veterinary research communications · 2025Article
- Abundance measurements reveal the balance between lysis and lysogeny in the human gut microbiome.Current biology : CB · 2025Article
- Optimizing phage therapy with artificial intelligence: a perspective.Frontiers in cellular and infection microbiology · 2025Review
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6 authors.
Funding
Abstract
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51
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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.