{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T18:30:10Z","timestamp":1791829810353,"version":"4.3.4"},"reference-count":113,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>As the crisis of antibiotic resistance escalates, phage therapy has regained attention as an alternative strategy. Artificial intelligence (AI) technologies offer new avenues to overcome the bottlenecks inherent in traditional bacteriophage research. This review summarizes the multi-dimensional innovative applications of machine learning, deep learning, and large biological models in phage studies. In the fields of phage recognition and genomics, support vector machines (SVMs), convolutional neural networks (CNNs), and pre-trained protein language models can all achieve recognition accuracy rates of over 90%. Furthermore, tools such as DeepHost and VirSorter2 can efficiently identify phage sequences, annotate functional genes, and predict hosts at the species or strain levels. For clinical translation, AI integrates patient characteristics, bacterial phenotypes, and phage profiles to customize cocktail regimens for individualized phage therapy. Graph neural network-based models like DeepPBI-KG integrate multi-omics knowledge graphs to precisely predict phage-host interactions (PHIs), whereas agent-based simulation and defense protein predictors forecast phage resistance evolution. Additionally, generative AI can support the de novo design of functional phage genomes and mine massive unannotated virome dark matter. Nevertheless, this cross-disciplinary field faces significant constraints, including uneven and biased sequencing datasets, insufficient model interpretability, and dual-use biosafety ethical risks accompanied by unclear algorithm accountability and incomplete global supervision systems. Future research should optimize standardized multimodal databases, develop explainable AI algorithms, and establish cross-disciplinary ethical governance frameworks to facilitate closed-loop verification between computational prediction and wet-lab experiments. In conclusion, the deep integration of AI and phage biology provides revolutionary strategies to tackle multidrug-resistant infections and advances the clinical transformation of phage precision medicine.<\/jats:p>","DOI":"10.3390\/microorganisms14092013","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:38:39Z","timestamp":1789040319000},"page":"2013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2375-211X","authenticated-orcid":false,"given":"Dapeng","family":"Yang","sequence":"first","affiliation":[{"name":"College of Agriculture and Biology, Liaocheng University, Liaocheng 252000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Agriculture and Biology, Liaocheng University, Liaocheng 252000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/S0140-6736(24)01867-1","article-title":"Global burden of bacterial antimicrobial resistance 1990-2021: Asystematic analysis with forecasts to 2050","volume":"404","author":"Naghavi","year":"2024","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cell.2022.11.017","article-title":"Phage Therapy: From Biological Mechanisms to Future Directions","volume":"186","author":"Strathdee","year":"2023","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e00066-18","DOI":"10.1128\/CMR.00066-18","article-title":"Phage Therapy in the Postantibiotic Era","volume":"32","author":"Altamirano","year":"2019","journal-title":"Clin. Microbiol. Rev."},{"key":"ref_4","first-page":"578","article-title":"AI-Assisted Phage Formulation Delivered via Injectable Hydrogels for Localized Control of Implant-Associated Infections","volume":"63","author":"Bayat","year":"2026","journal-title":"Bioact. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Piracha, Z.Z., and Saeed, U. (2026). Next-Generation Bacteriophage Therapeutic Systems: CRISPR-Based Engineering, near-Infrared Bioimaging, and Precision Strategies for Treating Multidrug-Resistant and Extensively Drug-Resistant Bacterial Infections. Front. Microbiol., 17.","DOI":"10.3389\/fmicb.2026.1748742"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chakraborty, C., Bhattacharya, M., and Lee, S.-S. (2026). The Transformative Impact of AI-Enabled AlphaFold 3: Evolution, Current Status, and Future Prospects in Structural Biology. Front. Artif. Intell., 9.","DOI":"10.3389\/frai.2026.1739303"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.tig.2026.01.013","article-title":"Machine Learning for Evolutionary Genetics and Molecular Evolution","volume":"42","author":"Svetec","year":"2026","journal-title":"Trends Genet."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fortaleza, J.A.G., Cabuhat, K.S.P., Lagunzad, H.C., Panizales, W.B., Cruz, J.T.P., Matamis, J.G., Mamaat, J.E.R., Libres, A.C., Dulay, R.M.R., and Nuevo, J.J.M. (2026). Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities. Antibiotics, 15.","DOI":"10.3390\/antibiotics15070635"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"giaf037","DOI":"10.1093\/gigascience\/giaf037","article-title":"HVSeeker: A Deep-Learning-Based Method for Identification of Host and Viral DNA Sequences","volume":"14","author":"Hauns","year":"2025","journal-title":"Gigascience"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1021\/acs.chemrev.4c00681","article-title":"Engineering Phages to Fight Multidrug-Resistant Bacteria","volume":"125","author":"Peng","year":"2025","journal-title":"Chem. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"140406","DOI":"10.1016\/j.bbapap.2020.140406","article-title":"Review and Comparative Analysis of Machine Learning-Based Phage Virion Protein Identification Methods","volume":"1868","author":"Meng","year":"2020","journal-title":"Biochim. Biophys. Acta (BBA) Proteins Proteom."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Mao, M., Zhang, R., Liao, Y.-T., and Wu, V.C.H. (2024). DeepPL: A Deep-Learning-Based Tool for the Prediction of Bacteriophage Lifecycle. PLoS Comput. Biol., 20.","DOI":"10.1371\/journal.pcbi.1012525"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112755","DOI":"10.1016\/j.knosys.2024.112755","article-title":"Multi-View Attention Graph Convolutional Networks for the Host Prediction of Phages","volume":"308","author":"Ma","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"giz066","DOI":"10.1093\/gigascience\/giz066","article-title":"PPR-Meta: A Tool for Identifying Phages and Plasmids from Metagenomic Fragments Using Deep Learning","volume":"8","author":"Fang","year":"2019","journal-title":"GigaScience"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1109\/TCBB.2020.3017386","article-title":"A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction","volume":"18","author":"Li","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1109\/TCBB.2022.3161135","article-title":"Virsearcher: Identifying Bacteriophages from Metagenomes by Combining Convolutional Neural Network and Gene Information","volume":"20","author":"Liu","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"bbab385","DOI":"10.1093\/bib\/bbab385","article-title":"DeepHost: Phage Host Prediction with Convolutional Neural Network","volume":"23","author":"Ruohan","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s40168-017-0283-5","article-title":"VirFinder: A Novel k-Mer Based Tool for Identifying Viral Sequences from Assembled Metagenomic Data","volume":"5","author":"Ren","year":"2017","journal-title":"Microbiome"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s40484-019-0187-4","article-title":"Identifying Viruses from Metagenomic Data Using Deep Learning","volume":"8","author":"Ren","year":"2020","journal-title":"Quant. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s40168-020-00990-y","article-title":"VirSorter2: A Multi-Classifier, Expert-Guided Approach to Detect Diverse DNA and RNA Viruses","volume":"9","author":"Guo","year":"2021","journal-title":"Microbiome"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1186\/s13059-025-03733-0","article-title":"Highly accurate prophage island detection with PIDE","volume":"26","author":"Gao","year":"2025","journal-title":"Genome Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s41579-019-0311-5","article-title":"Phage Diversity, Genomics and Phylogeny","volume":"18","author":"Dion","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e121","DOI":"10.1093\/nar\/gkaa856","article-title":"Seeker: Alignment-Free Identification of Bacteriophage Genomes by Deep Learning","volume":"48","author":"Auslander","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"i25","DOI":"10.1093\/bioinformatics\/btab293","article-title":"Bacteriophage Classification for Assembled Contigs Using Graph Convolutional Network","volume":"37","author":"Shang","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"bbaf626","DOI":"10.1093\/bib\/bbaf626","article-title":"From Genomic Signals to Prediction Tools: A Critical Feature Analysis and Rigorous Benchmark for Phage-Host Prediction","volume":"26","author":"Shang","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TCBB.2024.3488059","article-title":"LHPre: Phage Host Prediction With VAE-Based Class Imbalance Correction and Lyase Sequence Embedding","volume":"22","author":"Wang","year":"2025","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00203-026-04830-9","article-title":"From Structure to Design: Experimental and AI-Driven Approaches in Receptor-Binding Protein Engineering for Reprogramming Phage Host Range","volume":"208","author":"Xu","year":"2026","journal-title":"Arch. Microbiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1038\/s41598-021-81063-4","article-title":"Predicting Bacteriophage Hosts Based on Sequences of Annotated Receptor-Binding Proteins","volume":"11","author":"Boeckaerts","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1109\/JBHI.2025.3582652","article-title":"Phage Host Prediction Using Deep Neural Network With Multi-Source Protein Language Models and Squeeze-and-Excitation Attention Mechanism","volume":"30","author":"Gao","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cantu, V.A., Salamon, P., Seguritan, V., Redfield, J., Salamon, D., Edwards, R.A., and Segall, A.M. (2020). PhANNs, a Fast and Accurate Tool and Web Server to Classify Phage Structural Proteins. PLoS Comput. Biol., 16.","DOI":"10.1101\/2020.04.03.023523"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"jkab074","DOI":"10.1093\/g3journal\/jkab074","article-title":"MultiPhATE2: Code for Functional Annotation and Comparison of Phage Genomes","volume":"11","author":"Zhou","year":"2021","journal-title":"G3"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6372","DOI":"10.1021\/acs.jpcb.2c04346","article-title":"AlphaFold, Artificial Intelligence (AI), and Allostery","volume":"126","author":"Nussinov","year":"2022","journal-title":"J. Phys. Chem. B"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.3390\/cimb45040240","article-title":"Using AlphaFold Predictions in Viral Research","volume":"45","author":"Gutnik","year":"2023","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s40168-020-00867-0","article-title":"VIBRANT: Automated Recovery, Annotation and Curation of Microbial Viruses, and Evaluation of Viral Community Function from Genomic Sequences","volume":"8","author":"Kieft","year":"2020","journal-title":"Microbiome"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1111\/1462-2920.12923","article-title":"Phage-Bacteria Interaction Network in Human Oral Microbiome","volume":"18","author":"Wang","year":"2016","journal-title":"Environ. Microbiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1093\/bioinformatics\/btab845","article-title":"Virtifier: A Deep Learning-Based Identifier for Viral Sequences from Metagenomes","volume":"38","author":"Miao","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nami, Y., Imeni, N., and Panahi, B. (2021). Application of Machine Learning in Bacteriophage Research. BMC Microbiol., 21.","DOI":"10.1186\/s12866-021-02256-5"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"eaeb2362","DOI":"10.1126\/sciadv.aeb2362","article-title":"PRISM: A Unified Platform for Phage Isolation and Characterization from Single-Droplet Microenvironments","volume":"12","author":"Zhang","year":"2026","journal-title":"Sci. Adv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Le Bris, J., Chen, N., Supandy, A., Rendueles, O., and Van Tyne, D. (2025). Phage Therapy for Klebsiella Pneumoniae: Understanding Bacteria\u2013Phage Interactions for Therapeutic Innovations. PLoS Pathog., 21.","DOI":"10.1371\/journal.ppat.1012971"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"bbag085","DOI":"10.1093\/bib\/bbag085","article-title":"Towards Accurate Artificial Intelligence Models for Strain-Level Phage-Host Prediction","volume":"27","author":"Malajczuk","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1093\/femsre\/fuv048","article-title":"Computational Approaches to Predict Bacteriophage-Host Relationships","volume":"40","author":"Edwards","year":"2016","journal-title":"FEMS Microbiol. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Villarroel, J., Kleinheinz, K.A., Jurtz, V.I., Zschach, H., Lund, O., Nielsen, M., and Larsen, M.V. (2016). HostPhinder: A Phage Host Prediction Tool. Viruses, 8.","DOI":"10.3390\/v8050116"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Li, Z. (2023). RF_phage Virion: Classification of Phage Virion Proteins with a Random Forest Model. Front. Genet., 13, Erratum in Front. Genet.\u00a02023, 14, 1224665.","DOI":"10.3389\/fgene.2022.1103783"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1080\/00273171.2022.2146638","article-title":"Gradient Tree Boosting for Hierarchical Data","volume":"58","author":"Salditt","year":"2023","journal-title":"Multivar. Behav. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e0264122","DOI":"10.1128\/spectrum.02641-22","article-title":"SourceFinder: A Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies","volume":"10","author":"Grigorjev","year":"2022","journal-title":"Microbiol. Spectr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"110559","DOI":"10.1016\/j.virol.2025.110559","article-title":"Exploring Deep Learning in Phage Discovery and Characterization","volume":"609","author":"Silva","year":"2025","journal-title":"Virology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"btaf085","DOI":"10.1093\/bioinformatics\/btaf085","article-title":"PharaCon: A New Framework for Identifying Bacteriophages via Conditional Representation Learning","volume":"41","author":"Bai","year":"2025","journal-title":"Bioinformatics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s10989-019-09858-8","article-title":"Comparison of High-Throughput Sequencing for Phage Display Peptide Screening on Two Commercially Available Platforms","volume":"26","author":"Tajiri","year":"2020","journal-title":"Int. J. Pept. Res. Ther."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"W74","DOI":"10.1093\/nar\/gkz380","article-title":"Prophage Hunter: An Integrative Hunting Tool for Active Prophages","volume":"47","author":"Song","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"lqaf134","DOI":"10.1093\/nargab\/lqaf134","article-title":"Phage Evolutionary Relationships Emerge from Protein Language Model-Based Proteome Representation","volume":"7","author":"Panigrahi","year":"2025","journal-title":"NAR. Genom. Bioinform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"btaf016","DOI":"10.1093\/bioinformatics\/btaf016","article-title":"PHIStruct: Improving Phage-Host Interaction Prediction at Low Sequence Similarity Settings Using Structure-Aware Protein Embeddings","volume":"41","author":"Gonzales","year":"2024","journal-title":"Bioinformatics"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"110016","DOI":"10.1016\/j.virol.2024.110016","article-title":"A Comparative Analysis of Phage Classification Methods in Light of the Recent ICTV Taxonomic Revisions","volume":"594","author":"Zhu","year":"2024","journal-title":"Virology"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jbiosc.2025.05.004","article-title":"Discovery and Affinity Maturation of Antibody Fragments from an Unfavorably Enriched Phage Display Selection by Deep Sequencing and Machine Learning","volume":"140","author":"Kawada","year":"2025","journal-title":"J. Biosci. Bioeng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"71","DOI":"10.63075\/vkenhz09","article-title":"Phage Protein Classification Using Advanced Sequence Encoding: Performance Comparison of Deep Learning and Classical Machine Learning Approaches","volume":"3","author":"Khan","year":"2025","journal-title":"Annu. Methodol. Arch. Res. Rev."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ru, X., Li, L., and Wang, C. (2019). Identification of Phage Viral Proteins With Hybrid Sequence Features. Front. Microbiol., 10.","DOI":"10.3389\/fmicb.2019.00507"},{"key":"ref_57","first-page":"bbaf619","article-title":"In Silico Approaches for Discovering Microbial Antiviral Defense Systems","volume":"26","author":"Jiang","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4439","DOI":"10.1038\/s41467-026-70613-x","article-title":"High-Resolution Phage-Host Assignment through Key Proteins Using Large Language Models","volume":"17","author":"Du","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1109\/TCBBIO.2025.3559571","article-title":"BERTPVP: Identifying and Classifying Phage Virion Proteins Using Bidirectional Encoder Representations-Based Transformers","volume":"22","author":"Ma","year":"2025","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"bbaf285","DOI":"10.1093\/bib\/bbaf285","article-title":"ProtPhage: A Deep Learning Framework for Phage Viral Protein Identification and Functional Annotation","volume":"26","author":"Ou","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Albin, D., Ramsahoye, M., Kochavi, E., and Alistar, M. (2024). PhageScanner: A Reconfigurable Machine Learning Framework for Bacteriophage Genomic and Metagenomic Feature Annotation. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1446097"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"eadv7924","DOI":"10.1126\/science.adv7924","article-title":"DefensePredictor: A Machine Learning Model to Discover Prokaryotic Immune Systems","volume":"392","author":"DeWeirdt","year":"2026","journal-title":"Science"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.coviro.2021.11.005","article-title":"European Regulatory Aspects of Phage Therapy: Magistral Phage Preparations","volume":"52","author":"Verbeken","year":"2022","journal-title":"Curr. Opin. Virol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"9987","DOI":"10.1038\/s41467-024-53994-9","article-title":"A Blueprint for Broadly Effective Bacteriophage-Antibiotic Cocktails against Bacterial Infections","volume":"15","author":"Kim","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"bbac574","DOI":"10.1093\/bib\/bbac574","article-title":"Advances in the Field of Phage-Based Therapy with Special Emphasis on Computational Resources","volume":"24","author":"Bajiya","year":"2023","journal-title":"Brief. Bioinform."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, D., Zhang, X., and Zhang, X. (2024). Artificial Intelligence Applications in the Diagnosis and Treatment of Bacterial Infections. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1449844"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41746-025-01696-x","article-title":"Artificial Intelligence Model to Predict Resistances in Gram-Negative Bloodstream Infections","volume":"8","author":"Bonazzetti","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"167772","DOI":"10.1016\/j.bbadis.2025.167772","article-title":"A New Strategy to HER2-Specific Antibody Discovery through Artificial Intelligence-Powered Phage Display Screening Based on the Trastuzumab Framework","volume":"1871","author":"Zhang","year":"2025","journal-title":"Biochim. Biophys. Acta Mol. Basis Dis."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"S337","DOI":"10.1093\/cid\/ciad514","article-title":"Towards Standardization of Phage Susceptibility Testing: The Israeli Phage Therapy Center \u201cClinical Phage Microbiology\u201d-A Pipeline Proposal","volume":"77","author":"Yerushalmy","year":"2023","journal-title":"Clin. Infect. Dis."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Leite, D.M.C., Brochet, X., Resch, G., Que, Y.-A., Neves, A., and Pe\u00f1a-Reyes, C. (2018). Computational Prediction of Inter-Species Relationships through Omics Data Analysis and Machine Learning. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2388-7"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"lqaa044","DOI":"10.1093\/nargab\/lqaa044","article-title":"A Network-Based Integrated Framework for Predicting Virus-Prokaryote Interactions","volume":"2","author":"Wang","year":"2020","journal-title":"NAR. Genom. Bioinform."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Shang, J., and Sun, Y. (2021). Predicting the Hosts of Prokaryotic Viruses Using GCN-Based Semi-Supervised Learning. BMC Biol., 19.","DOI":"10.1186\/s12915-021-01180-4"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1016\/j.csbj.2023.06.014","article-title":"GSPHI: A Novel Deep Learning Model for Predicting Phage-Host Interactions via Multiple Biological Information","volume":"21","author":"Pan","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1093\/cid\/ciad335","article-title":"A Retrospective, Observational Study of 12 Cases of Expanded-Access Customized Phage Therapy: Production, Characteristics, and Clinical Outcomes","volume":"77","author":"Green","year":"2023","journal-title":"Clin. Infect. Dis."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"bbae484","DOI":"10.1093\/bib\/bbae484","article-title":"DeepPBI-KG: A Deep Learning Method for the Prediction of Phage-Bacteria Interactions Based on Key Genes","volume":"25","author":"Wei","year":"2024","journal-title":"Brief. Bioinform."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bal\u00e1\u017e, A., Kajsik, M., Budi\u0161, J., Szemes, T., and Tur\u0148a, J. (2023). PHERI-Phage Host ExploRation Pipeline. Microorganisms, 11.","DOI":"10.3390\/microorganisms11061398"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4106","DOI":"10.1038\/s41598-022-08173-5","article-title":"SCORPION Is a Stacking-Based Ensemble Learning Framework for Accurate Prediction of Phage Virion Proteins","volume":"12","author":"Ahmad","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"128963","DOI":"10.1016\/j.eswa.2025.128963","article-title":"CM-PHI: Combining Multi-Hop Attention Graph Neural Network with Sequence Semantic Analysis to Predict Phage-Host Interaction","volume":"296","author":"Pan","year":"2026","journal-title":"Expert. Syst. Appl."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Natarajan, P.M., Varma, S.R., Kodangattil Narayanan, J., and Odeh, R. (2026). Predicting Inter-Microbial Host Specificity in Oral Biofilms Using a Lightweight Relation-Aware Knowledge Graph Model. Front. Cell Infect. Microbiol., 16.","DOI":"10.3389\/fcimb.2026.1775191"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3772","DOI":"10.1109\/TCBB.2023.3322870","article-title":"Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism","volume":"20","author":"Ma","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"bbaf017","DOI":"10.1093\/bib\/bbaf017","article-title":"PHPGAT: Predicting Phage Hosts Based on Multimodal Heterogeneous Knowledge Graph with Graph Attention Network","volume":"26","author":"Liu","year":"2024","journal-title":"Brief. Bioinform."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ming, Z., Chen, X., Wang, S., Liu, H., Yuan, Z., Wu, M., and Xia, H. (2023). HostNet: Improved Sequence Representation in Deep Neural Networks for Virus-Host Prediction. BMC Bioinform., 24.","DOI":"10.1186\/s12859-023-05582-9"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"4355","DOI":"10.1038\/s41467-024-48675-6","article-title":"Prediction of Klebsiella Phage-Host Specificity at the Strain Level","volume":"15","author":"Boeckaerts","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1038\/s41564-024-01832-5","article-title":"Prediction of Strain Level Phage-Host Interactions across the Escherichia Genus Using Only Genomic Information","volume":"9","author":"Gaborieau","year":"2024","journal-title":"Nat. Microbiol."},{"key":"ref_85","first-page":"100575","article-title":"Phage P100 Resistance in Clinical and Foodborne Listeria Monocytogenes Isolates Is Associated with Adsorption-Inhibiting Mutations and Fitness Trade-Offs","volume":"10","author":"Brieske","year":"2026","journal-title":"Curr. Res. Microb. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"7043","DOI":"10.1038\/s41467-026-72596-1","article-title":"Lifestyle-Dependent Evolution and CtrA-Mediated Lifestyle Transitions Shape Phage Resistance in Marine Roseobacter","volume":"17","author":"Li","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"wrag077","DOI":"10.1093\/ismejo\/wrag077","article-title":"Phage Receptor Specificity Drives Cross-Resistance Patterns and Governs Fitness Trade-Offs during Sequential Resistance Acquisition in Salmonella","volume":"20","author":"Wu","year":"2026","journal-title":"ISME J."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1038\/s41467-025-61055-y","article-title":"Phage-Mediated Peripheral Kill-the-Winner Facilitates the Maintenance of Costly Antibiotic Resistance","volume":"16","author":"Ruan","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Evseev, P., Shneider, M., and Miroshnikov, K. (2022). Evolution of Phage Tail Sheath Protein. Viruses, 14.","DOI":"10.3390\/v14061148"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Matusiak, R., Antczak, M., Kawa, A., Paszkiewicz, M., Witaszewska, J., Kazimierczak, J., and W\u00f3jcik, E.A. (2025). Revisiting Phage Tail Spike Architecture: Evidence for Undetected Receptor-Binding Proteins in Caudoviricetes with Non-Contractile Tails. Front. Microbiol., 16.","DOI":"10.3389\/fmicb.2025.1625765"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"bbaf656","DOI":"10.1093\/bib\/bbaf656","article-title":"PBIP: A Deep Learning Framework for Predicting Phage-Bacterium Interactions at the Strain Level","volume":"26","author":"Ma","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Bodaka, S., and Kolliputi, N. (2025). CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple Feature Representation to Predict Hosts of Bacteriophages. Front. Bioinform., 5.","DOI":"10.3389\/fbinf.2025.1622931"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhao, Z., Li, M., Song, W., Xiao, M., and Fang, M. (2025). MoEPH: An Adaptive Fusion-Based LLM for Predicting Phage-Host Interactions in Health Informatics. Front. Microbiol., 16.","DOI":"10.3389\/fmicb.2025.1634705"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"btag262","DOI":"10.1093\/bioinformatics\/btag262","article-title":"PhageMind: Generalized Strain-Level Phage Host Range Prediction via Meta-Learning","volume":"42","author":"Shen","year":"2026","journal-title":"Bioinformatics"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.gpb.2022.02.003","article-title":"PHISDetector: A Tool to Detect Diverse In Silico Phage-Host Interaction Signals for Virome Studies","volume":"20","author":"Zhou","year":"2022","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"111754","DOI":"10.1016\/j.mehy.2025.111754","article-title":"Automated Phage Therapy in Smart Hospitals: A Hypothesis for an AI, Metagenomics, and IoT Pipeline to Treat Resistant Infections","volume":"203","author":"Rahimian","year":"2025","journal-title":"Med. Hypotheses"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1038\/s41598-025-32359-2","article-title":"MVPHI: A Multi-View Learning Framework for Predicting Complex Microbial Interactions","volume":"16","author":"Xie","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Doud, M.B., Robertson, J.M., and Strathdee, S.A. (2025). Optimizing Phage Therapy with Artificial Intelligence: A Perspective. Front. Cell Infect. Microbiol., 15.","DOI":"10.3389\/fcimb.2025.1611857"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Wu, P., Li, W., Zhang, W., Li, S., Deng, B., Xu, S., and Li, Z. (2025). Advanced Strategies in Phage Research: Innovations, Applications, and Challenges. Microorganisms, 13.","DOI":"10.3390\/microorganisms13081960"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"veae104","DOI":"10.1093\/ve\/veae104","article-title":"Inferring Strain-Level Mutational Drivers of Phage-Bacteria Interaction Phenotypes Arising during Coevolutionary Dynamics","volume":"10","author":"Peng","year":"2024","journal-title":"Virus Evol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/j.clinthera.2020.07.014","article-title":"Phage Therapy in the Resistance Era: Where Do We Stand and Where Are We Going?","volume":"42","author":"Luong","year":"2020","journal-title":"Clin. Ther."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Liu, K., Wang, C., Zhou, X., Guo, X., Yang, Y., Liu, W., Zhao, R., and Song, H. (2024). Bacteriophage Therapy for Drug-Resistant Staphylococcus Aureus Infections. Front. Cell Infect. Microbiol., 14.","DOI":"10.3389\/fcimb.2024.1336821"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ataee, S., Brochet, X., and Pe\u00f1a-Reyes, C.A. (2022). Bacteriophage Genetic Edition Using LSTM. Front. Bioinform., 2.","DOI":"10.3389\/fbinf.2022.932319"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.chom.2024.01.015","article-title":"Bacterial Defense Systems Exhibit Synergistic Anti-Phage Activity","volume":"32","author":"Wu","year":"2024","journal-title":"Cell Host Microbe"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Hasan, M., and Ahn, J. (2022). Evolutionary Dynamics between Phages and Bacteria as a Possible Approach for Designing Effective Phage Therapies against Antibiotic-Resistant Bacteria. Antibiotics, 11.","DOI":"10.3390\/antibiotics11070915"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"20240474","DOI":"10.1098\/rstb.2024.0474","article-title":"Phage Provoke Growth Delays and SOS Response Induction despite CRISPR-Cas Protection","volume":"380","author":"Pons","year":"2025","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"bbaf014","DOI":"10.1093\/bib\/bbaf014","article-title":"GOPhage: Protein Function Annotation for Bacteriophages by Integrating the Genomic Context","volume":"26","author":"Guan","year":"2024","journal-title":"Brief. Bioinform."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"bbad408","DOI":"10.1093\/bib\/bbad408","article-title":"PhaGenus: Genus-Level Classification of Bacteriophages Using a Transformer Model","volume":"24","author":"Guan","year":"2023","journal-title":"Brief. Bioinform."},{"key":"ref_109","unstructured":"Shang, J., Peng, C., Guan, J., Cai, D., Wang, D., and Sun, Y. (2025). Computational Approaches for Virus Host Prediction: A Review of Methods and Applications. arXiv."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"eado9336","DOI":"10.1126\/science.ado9336","article-title":"Sequence Modeling and Design from Molecular to Genome Scale with Evo","volume":"386","author":"Nguyen","year":"2024","journal-title":"Science"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/d41586-025-03055-y","article-title":"World\u2019s First AI-Designed Viruses a Step towards AI-Generated Life","volume":"646","author":"Kavanagh","year":"2025","journal-title":"Nature"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1038\/s41586-026-10176-5","article-title":"Genome Modelling and Design across All Domains of Life with Evo 2","volume":"652","author":"Brixi","year":"2026","journal-title":"Nature"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1038\/s41467-026-69098-5","article-title":"Uncovering Cas9 PAM Diversity through Metagenomic Mining and Machine Learning","volume":"17","author":"Fang","year":"2026","journal-title":"Nat. Commun."}],"container-title":["Microorganisms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2013\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:49:21Z","timestamp":1789040961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":113,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["microorganisms14092013"],"URL":"https:\/\/doi.org\/10.3390\/microorganisms14092013","relation":{},"ISSN":["2076-2607"],"issn-type":[{"value":"2076-2607","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]}}}