ArticlePHAGE (New Rochelle, N.Y.)2022
vHULK, a New Tool for Bacteriophage Host Prediction Based on Annotated Genomic Features and Neural Networks.
Article in PHAGE (New Rochelle, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 3 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
- Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.Briefings in bioinformatics · 2024Pooled it
- Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes using machine learning.Nature microbiology · 2026Article
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction.PLoS computational biology · 2026Article
- High-resolution phage-host assignment through key proteins using large language models.Nature communications · 2026Article
- Bacteriophages in gut metagenomes: from analysis to application.Virology journal · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Viromics approaches for the study of viral diversity and ecology in microbiomes.Nature reviews. Genetics · 2026Review
- FusionPHI: A phage-host interaction prediction network model based on attention-driven multi-modal feature fusion.PloS one · 2026Article
- Unexplored diversity and molecular genetic signatures of chimallin and phuz encoding phages.BMC genomics · 2025Article
- A machine learning approach to predict strain-specific phage-host interactions.Scientific reports · 2025Article
- The Kocurious case of Noodlococcus: genomic insights intoMicrobial genomics · 2025Article
- Gut phages and their interactions with bacterial and mammalian hosts.Journal of bacteriology · 2025Review
- Review
- Phage therapy for environmental biotechnology applications.Frontiers in microbiology · 2025Review
- Predicting phage-host interactions via feature augmentation and regional graph convolution.Briefings in bioinformatics · 2024Article
- A novel framework for phage-host prediction via logical probability theory and network sparsification.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The experimental determination of a bacteriophage host is a laborious procedure. Thus, there is a pressing need for reliable computational predictions of bacteriophage hosts. Materials and Methods: We developed the program vHULK for phage host prediction based on 9504 phage genome features, which consider alignment significance scores between predicted proteins and a curated database of viral protein families. The features were fed to a neural network, and two models were trained to predict 77 host genera and 118 host species. Results: In controlled random test sets with 90% redundancy reduction in terms of protein similarity, vHULK obtained on average 83% precision and 79% recall at the genus level, and 71% precision and 67% recall at the species level. The performance of vHULK was compared against three other tools on a test data set with 2153 phage genomes. On this data set, vHULK achieved better performance at both the genus and the species levels than the other tools. Conclusions: Our results suggest that vHULK represents an advance on the state of art in phage host prediction.
Indexed as
Identifiers
What OpenQuestion holds
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.