ArticleFrontiers in microbiology2023
An ensemble method for prediction of phage-based therapy against bacterial infections.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 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
- Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.Briefings in bioinformatics · 2024Pooled it
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Phage therapy: from basic biology to clinical application.Nature reviews. Microbiology · 2026Review
- Genomic landscape of 340 virulent Acinetobacter bacteriophages reveals anti-CRISPR-enriched candidates for therapeutic prioritization.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- FusionPHI: A phage-host interaction prediction network model based on attention-driven multi-modal feature fusion.PloS one · 2026Article
- NRG-P0074 Viral Sample RU1 from UnclassifiedPHAGE (New Rochelle, N.Y.) · 2025Article
- Prediction of exosomal miRNA-based biomarkers for liquid biopsy.Scientific reports · 2025Article
- Regulatory Landscape and the Potential of Bacteriophage Applications in the United States' Food Industry.Journal of food protection · 2025Review
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- Leveraging machine learning and big data techniques to map the global patent landscape of phage therapy.Nature biotechnology · 2024Article
- Genomic insight on Klebsiella variicola isolated from wastewater treatment plant has uncovered a novel bacteriophage.BMC genomics · 2024Article
- Viromic and Metagenomic Analyses of Commercial Spirulina Fermentations Reveal Remarkable Microbial Diversity.Viruses · 2024Article
- A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.Frontiers in molecular biosciences · 2024Article
- Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation.Scientific reports · 2023Article
- Automating Predictive Phage Therapy Pharmacology.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1,201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4-66.2% for BLASTPhage, 55-78.4% for BLASTHost, and 43.7-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9, 80.6, 85.5, 90, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/) for the same.
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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.