ArticleComputational and structural biotechnology journal2023
GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 5 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 5 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
- Evolutionary dynamics and research hotspots of phage applications againstFrontiers in microbiology · 2026Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 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
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- From structure to design: experimental and AI-driven approaches in receptor-binding protein engineering for reprogramming phage host range.Archives of microbiology · 2026Review
- Nano-Enabled Delivery of Phage-Based Antibacterials Against ESKAPE Pathogens.Pharmaceutics · 2026Review
- Predicting inter-microbial host specificity in oral biofilms using a lightweight relation-aware knowledge graph model.Frontiers in cellular and infection microbiology · 2026Article
- MVPHI: a multi-view learning framework for predicting complex microbial interactions.Scientific reports · 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
- NRG-P0074 Viral Sample RU1 from UnclassifiedPHAGE (New Rochelle, N.Y.) · 2025Article
- Advanced Strategies in Phage Research: Innovations, Applications, and Challenges.Microorganisms · 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
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- A novel framework for phage-host prediction via logical probability theory and network sparsification.Briefings in bioinformatics · 2024Article
- Prediction of strain level phage-host interactions across the Escherichia genus using only genomic information.Nature microbiology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.
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