ArticleIEEE/ACM transactions on computational biology and bioinformatics
A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
What it found
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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.
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
35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- 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
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.Briefings in bioinformatics · 2024Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- 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
- FusionPHI: A phage-host interaction prediction network model based on attention-driven multi-modal feature fusion.PloS one · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Artificial Intelligence Applications in Bacteriophage Genomics and Host Interaction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
- In silico approaches for discovering microbial antiviral defense systems.Briefings in bioinformatics · 2025Review
- A machine learning approach to predict strain-specific phage-host interactions.Scientific reports · 2025Article
- Gut phages and their interactions with bacterial and mammalian hosts.Journal of bacteriology · 2025Review
- Factors Affecting Phage-Bacteria Coevolution Dynamics.Viruses · 2025Review
- Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique.iScience · 2025Article
- MoEPH: an adaptive fusion-based LLM for predicting phage-host interactions in health informatics.Frontiers in microbiology · 2025Article
- CoMPHI: a novel composite machine learning approach utilizing multiple feature representation to predict hosts of bacteriophages.Frontiers in bioinformatics · 2025Article
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.
Indexed as
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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.