{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T17:14:14Z","timestamp":1788974054409,"version":"build-2803163510"},"reference-count":181,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    In precision phage therapy, artificial intelligence and machine learning (AI\/ML) is being applied for phage detection, genomes\/proteome annotation, host prediction, resistance profiling and therapeutic optimization. Yet the landscape remains fragmented owing to a lack of systematic synthesis of its translational maturity, methodological robustness and pipeline coverage. We performed a systematic review of AI\/ML tools relevant to precision phage therapy. Web of Science, PubMed, MEDLINE, Scopus and IEEE Xplore were used to identify records. Of 6,969 identified records, 2,214 duplicates were removed, leaving 4,755 unique records for title\/abstract screening; 510 full-text articles were assessed for eligibility, and 128 studies were included in the final synthesis. Studies were classified across a nine-module precision phage therapy pipeline by architectural family, validation maturity, accessibility, translational readiness (AI_PTRL), and methodological quality using an adapted PROBAST framework. The 128 included studies were published between 2012 and 2025, with a median publication year of 2023 and peak activity during 2022\u20132025. The large majority were methodological\/tool development studies (115\/128, 89.8%) and these predominantly described\n                    <jats:italic>in silico<\/jats:italic>\n                    validation (101\/128, 78.9%). The pipeline coverage was uneven, with the highest concentration in the host range and interaction determinants (\n                    <jats:italic>M<\/jats:italic>\n                    4, 43\/128, 33.6%) and genome\/protein annotation and functional inference (\n                    <jats:italic>M<\/jats:italic>\n                    3, 33\/128, 25.8%). Safety screening, therapeutic design, and infrastructure modules were comparatively underrepresented. From an architectural standpoint, hybrid\/integrated systems (HIS) (39\/128, 30.5%) and classical machine learning (CML) (38\/128, 29.7%) were the most common approaches with substantial diversification after 2022. The overall translational readiness was modest (median AI_PTRL: 6, IQR 4\u20136). Methodological assessment demonstrated high evaluation risk of bias (104\/128, 81.2%) and substantial development concern (88\/128, 68.8%), especially in Domain 4 (analysis). AI\/ML for precision phage therapy is a rapidly growing and diversifying field, particularly in host\u2013phage interaction prediction, however efforts are still limited for downstream precise phage therapy tasks. Experimental validation and real-world deployment remain limited with methodological limitations due to analytical design and evaluative approaches. Future work should prioritize rigorous validation frameworks, higher-quality and more complete representative datasets, stronger safety screening, therapeutic optimization, and clinically actionable decision support.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2026.1873220","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:40:13Z","timestamp":1788972013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Precision phage therapy in the AI\/ML era: a systematic review of discovery-to-clinical translation evidence"],"prefix":"10.3389","volume":"17","author":[{"given":"Najwa Menwer","family":"Alharbi","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, Faculty of Science, King Abdulaziz University","place":["Jeddah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1038\/s41564-025-01935-7","article-title":"CRISPRi-ART enables functional genomics of diverse bacteriophages using RNA-binding dCas13d","volume":"10","author":"Adler","year":"2025","journal-title":"Nat. Microbiol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1148579","DOI":"10.3389\/fmicb.2023.1148579","article-title":"An ensemble method for prediction of phage-based therapy against bacterial infections","volume":"14","author":"Aggarwal","year":"2023","journal-title":"Front. Microbiol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1446097","DOI":"10.3389\/fmicb.2024.1446097","article-title":"PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation","volume":"15","author":"Albin","year":"2024","journal-title":"Front. Microbiol."},{"key":"B4","doi-asserted-by":"publisher","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":"Al-Najim","year":"2025","journal-title":"Gigascience"},{"key":"B5","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1089\/phage.2021.0016","article-title":"vHULK, a new tool for bacteriophage host prediction based on annotated genomic features and neural networks","volume":"3","author":"Amgarten","year":"2022","journal-title":"Phage Ther. Appl. Res."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1016\/j.ygeno.2019.09.006","article-title":"Pred-BVP-Unb: fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination","volume":"112","author":"Arif","year":"2020","journal-title":"Genomics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"ofaa389","DOI":"10.1093\/ofid\/ofaa389","article-title":"Lessons learned from the first 10 consecutive cases of intravenous bacteriophage therapy to treat multidrug-resistant bacterial infections at a single center in the United States","volume":"7","author":"Aslam","year":"2020","journal-title":"Open Forum Infect. Dis."},{"key":"B8","doi-asserted-by":"publisher","first-page":"932319","DOI":"10.3389\/fbinf.2022.932319","article-title":"Bacteriophage genetic edition using LSTM","volume":"2","author":"Ataee","year":"2022","journal-title":"Front. Bioinform."},{"key":"B9","doi-asserted-by":"publisher","first-page":"gkaa856","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":"B10","doi-asserted-by":"publisher","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":"B11","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.3390\/molecules28052238","article-title":"Prediction of phage virion proteins using machine learning methods","volume":"28","author":"Barman","year":"2023","journal-title":"Molecules"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e1011649","DOI":"10.1371\/journal.pcbi.1011649","article-title":"Virus-host interactions predictor (VHIP): machine learning approach to resolve microbial virus-host interaction networks","volume":"20","author":"Bastien","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1038\/s41592-024-02362-y","article-title":"Guiding questions to avoid data leakage in biological machine learning applications","volume":"21","author":"Bernett","year":"2024","journal-title":"Nat. Methods"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1622931","DOI":"10.3389\/fbinf.2025.1622931","article-title":"CoMPHI: a novel composite machine learning approach utilizing multiple feature representation to predict hosts of bacteriophages","volume":"5","author":"Bodaka","year":"2025","journal-title":"Front. Bioinform."},{"key":"B15","doi-asserted-by":"publisher","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":"B16","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.3390\/v14061329","article-title":"Identification of phage receptor-binding protein sequences with hidden markov models and an extreme gradient boosting classifier","volume":"14","author":"Boeckaerts","year":"2022","journal-title":"Viruses Basel"},{"key":"B17","doi-asserted-by":"publisher","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":"B18","doi-asserted-by":"publisher","first-page":"btaf531","DOI":"10.1093\/bioinformatics\/btaf531","article-title":"SPAED: harnessing AlphaFold output for accurate segmentation of phage endolysin domains","volume":"41","author":"Boulay","year":"","journal-title":"Bioinformatics"},{"key":"B19","doi-asserted-by":"publisher","first-page":"9114","DOI":"10.1038\/s41467-025-64177-5","article-title":"Empathi: embedding-based phage protein annotation tool by hierarchical assignment","volume":"16","author":"Boulay","year":"2025","journal-title":"Nat. Commun"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3389\/fmicb.2020.01161","article-title":"Good manufacturing practice (GMP) compliance for phage therapy medicinal products","volume":"11","author":"Bretaudeau","year":"2020","journal-title":"Front. Microbiol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"38249","DOI":"10.1038\/s41598-025-22075-2","article-title":"A machine learning approach to predict strain-specific phage-host interactions","volume":"15","author":"Camejo","year":"2025","journal-title":"Sci. Rep"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e1007845","DOI":"10.1371\/journal.pcbi.1007845","article-title":"PhANNs, a fast and accurate tool and web server to classify phage structural proteins","volume":"16","author":"Cantu","year":"2020","journal-title":"PLoS Comput. Biol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1038\/s41467-025-56989-2","article-title":"Benchmarking large language models for biomedical natural language processing applications and recommendations","volume":"16","author":"Chen","year":"","journal-title":"Nat. Commun."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1634705","DOI":"10.3389\/fmicb.2025.1634705","article-title":"MoEPH: an adaptive fusion-based LLM for predicting phage-host interactions in health informatics","volume":"16","author":"Chen","year":"","journal-title":"Front. Microbiol."},{"key":"B25","doi-asserted-by":"publisher","first-page":"e13404","DOI":"10.7717\/peerj.13404","article-title":"DeephageTP: a convolutional neural network framework for identifying phage-specific proteins from metagenomic sequencing data","volume":"10","author":"Chu","year":"2022","journal-title":"PeerJ"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e0088824","DOI":"10.1128\/msystems.00888-24","article-title":"MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes","volume":"9","author":"Coclet","year":"2024","journal-title":"mSystems"},{"key":"B27","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.coviro.2021.05.003","article-title":"Global overview and major challenges of host prediction methods for uncultivated phages","volume":"49","author":"Coclet","year":"2021","journal-title":"Curr. Opin. Virol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"e1011831","DOI":"10.1371\/journal.pcbi.1011831","article-title":"DepoScope: accurate phage depolymerase annotation and domain delineation using large language models","volume":"20","author":"Concha-Eloko","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"e78","DOI":"10.1017\/S2045796021000664","article-title":"The lack of statistical power of subgroup analyses in meta-analyses: a cautionary note","volume":"30","author":"Cuijpers","year":"2021","journal-title":"Epidemiol. Psychiatr. Sci."},{"key":"B30","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/v17030314","article-title":"High-performance genome annotation for a safer and faster-developing phage therapy","volume":"17","author":"Culot","year":"2025","journal-title":"Viruses"},{"key":"B31","doi-asserted-by":"publisher","first-page":"e00012","DOI":"10.1128\/MMBR.00012-19","article-title":"Pharmacologically aware phage therapy: pharmacodynamic and pharmacokinetic obstacles to phage antibacterial action in animal and human bodies","volume":"83","author":"Dabrowska","year":"2019","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"B32","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1111\/nep.13913","article-title":"Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)","volume":"26","author":"de Jong","year":"2021","journal-title":"Nephrology"},{"key":"B33","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/s41591-019-0437-z","article-title":"Engineered bacteriophages for treatment of a patient with a disseminated drug-resistant Mycobacterium abscessus","volume":"25","author":"Dedrick","year":"2019","journal-title":"Nat. Med."},{"key":"B34","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s41512-022-00126-w","article-title":"Risk of bias of prognostic models developed using machine learning: a systematic review in oncology","volume":"6","author":"Dhiman","year":"2022","journal-title":"Diagn. Progn. Res."},{"key":"B35","doi-asserted-by":"publisher","first-page":"2229","DOI":"10.1039\/C4MB00316K","article-title":"Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis","volume":"10","author":"Ding","year":"2014","journal-title":"Mol. Biosyst."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1611857","DOI":"10.3389\/fcimb.2025.1611857","article-title":"Optimizing phage therapy with artificial intelligence: a perspective","volume":"15","author":"Doud","year":"2025","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"e1011671","DOI":"10.1371\/journal.pcbi.1011671","article-title":"Prokaryotic virus host prediction with graph contrastive augmentaion","volume":"19","author":"Du","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"4698","DOI":"10.1093\/nar\/gkaa219","article-title":"Machine learning predicts new anti-CRISPR proteins","volume":"48","author":"Eitzinger","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B39","doi-asserted-by":"publisher","first-page":"443","DOI":"10.3390\/v16030443","article-title":"The medicinal phage-regulatory roadmap for phage therapy under EU pharmaceutical legislation","volume":"16","author":"Faltus","year":"2024","journal-title":"Viruses"},{"key":"B40","doi-asserted-by":"publisher","first-page":"giac076","DOI":"10.1093\/gigascience\/giac076","article-title":"DeePVP: identification and classification of phage virion proteins using deep learning","volume":"11","author":"Fang","year":"2022","journal-title":"Gigascience"},{"key":"B41","doi-asserted-by":"publisher","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":"B42","doi-asserted-by":"publisher","first-page":"615711","DOI":"10.3389\/fmicb.2021.615711","article-title":"VirionFinder: identification of complete and partial prokaryote virus virion protein from virome data using the sequence and biochemical properties of amino acids","volume":"12","author":"Fang","year":"2021","journal-title":"Front. Microbiol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1021\/acssynbio.5c00450","article-title":"Computational pipeline for targeted integration and variable payload expression in bacteriophage engineering","volume":"14","author":"Fernbach","year":"2025","journal-title":"ACS Synth. Biol."},{"key":"B44","doi-asserted-by":"publisher","first-page":"114583","DOI":"10.1016\/j.celrep.2024.114583","article-title":"DeepMineLys: deep mining of phage lysins from human microbiome","volume":"43","author":"Fu","year":"2024","journal-title":"Cell Rep."},{"key":"B45","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.1038\/s41564-024-01832-5","article-title":"Prediction of strain level phage\u2013host interactions across the Escherichia genus using only genomic information","volume":"9","author":"Gaborieau","year":"2024","journal-title":"Nat. Microbiol."},{"key":"B46","doi-asserted-by":"publisher","first-page":"3436","DOI":"10.1038\/s41598-019-39847-2","article-title":"Host taxon predictor: a tool for predicting taxon of the host of a newly discovered virus","volume":"9","author":"Ga\u010dan","year":"2019","journal-title":"Sci. Rep"},{"key":"B47","doi-asserted-by":"publisher","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","author":"Gao","year":"2026","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B48","doi-asserted-by":"publisher","first-page":"btaf460","DOI":"10.1093\/bioinformatics\/btaf460","article-title":"PDP-miner: an AI\/ML tool to detect prophage tail proteins with depolymerase domains across thousands of bacterial genomes","volume":"41","author":"Gauthier","year":"2025","journal-title":"Bioinformatics"},{"key":"B49","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/s41579-023-00934-x","article-title":"The highly diverse antiphage defence systems of bacteria","volume":"21","author":"Georjon","year":"2023","journal-title":"Nat. Rev. Microbiol."},{"key":"B50","doi-asserted-by":"publisher","first-page":"e0289030","DOI":"10.1371\/journal.pone.0289030","article-title":"Protein embeddings improve phage-host interaction prediction","volume":"18","author":"Gonzales","year":"2023","journal-title":"PLoS ONE"},{"key":"B51","doi-asserted-by":"publisher","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":"2025","journal-title":"Bioinformatics"},{"key":"B52","doi-asserted-by":"publisher","first-page":"e00066","DOI":"10.1128\/CMR.00066-18","article-title":"Phage therapy in the postantibiotic era","volume":"32","author":"Gordillo Altamirano","year":"2019","journal-title":"Clin. Microbiol. Rev."},{"key":"B53","doi-asserted-by":"publisher","first-page":"D491","DOI":"10.1093\/nar\/gkw975","article-title":"Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation","volume":"45","author":"Grazziotin","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"B54","doi-asserted-by":"publisher","first-page":"e0002225","DOI":"10.1128\/mmbr.00022-25","article-title":"Computational function prediction of bacteria and phage proteins","volume":"89","author":"Grigson","year":"2025","journal-title":"Microbiol. Mol. Biol. Rev. MMBR"},{"key":"B55","doi-asserted-by":"publisher","first-page":"S352","DOI":"10.1093\/cid\/ciad539","article-title":"Knowing and naming: phage annotation and nomenclature for phage therapy","volume":"77","author":"Grigson","year":"2023","journal-title":"Clin. Infect. Dis."},{"key":"B56","doi-asserted-by":"publisher","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":"2025","journal-title":"Brief. Bioinform."},{"key":"B57","doi-asserted-by":"publisher","first-page":"3784","DOI":"10.1038\/s41467-020-17652-0","article-title":"Machine-learning approach expands the repertoire of anti-CRISPR protein families","volume":"11","author":"Gussow","year":"2020","journal-title":"Nat. Commun."},{"key":"B58","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1038\/s41586-019-1894-8","article-title":"The arms race between bacteria and their phage foes","volume":"577","author":"Hampton","year":"2020","journal-title":"Nature"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.3390\/sym13081506","article-title":"iPVP-MCV: a multi-classifier voting model for the accurate identification of phage virion proteins","volume":"13","author":"Han","year":"2021","journal-title":"Symmetry"},{"key":"B60","doi-asserted-by":"publisher","first-page":"e70073","DOI":"10.1002\/imt2.70073","article-title":"PSOSP uncovers pervasive SOS-independent prophages with distinct genomic and host traits in bacterial genomes","volume":"4","author":"Hao","year":"2025","journal-title":"iMeta"},{"key":"B61","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1146\/annurev-med-080219-122208","article-title":"Phage therapy for antibiotic-resistant bacterial infections","volume":"73","author":"Hatfull","year":"2022","journal-title":"Annu. Rev. Med."},{"key":"B62","doi-asserted-by":"publisher","first-page":"giae062","DOI":"10.1093\/gigascience\/giae062","article-title":"Deepdefense: annotation of immune systems in prokaryotes using deep learning","volume":"13","author":"Hauns","year":"2024","journal-title":"Gigascience"},{"key":"B63","doi-asserted-by":"publisher","first-page":"1674","DOI":"10.3389\/fmicb.2019.01674","article-title":"The removal of endo- and enterotoxins from bacteriophage preparations","volume":"10","author":"Hietala","year":"2019","journal-title":"Front. Microbiol."},{"key":"B64","doi-asserted-by":"publisher","first-page":"e11396","DOI":"10.7717\/peerj.11396","article-title":"BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains","volume":"9","author":"Hockenberry","year":"2021","journal-title":"PEERJ"},{"key":"B65","doi-asserted-by":"publisher","first-page":"vead083","DOI":"10.1093\/ve\/vead083","article-title":"Computational host range prediction\u2014the good, the bad, and the ugly","volume":"10","author":"Howell","year":"2024","journal-title":"Virus Evol."},{"key":"B66","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/ph12010035","article-title":"Phages for phage therapy: isolation, characterization, and host range breadth","volume":"12","author":"Hyman","year":"2019","journal-title":"Pharmaceuticals"},{"key":"B67","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1038\/s41564-024-01712-y","article-title":"Personalized bacteriophage therapy for difficult-to-treat infections","volume":"9","author":"Iredell","year":"2024","journal-title":"Nat. Microbiol."},{"key":"B68","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/s44320-025-00119-z","article-title":"AlphaDesign: a de novo protein design framework based on AlphaFold","volume":"21","author":"Jendrusch","year":"2025","journal-title":"Mol. Syst. Biol."},{"key":"B69","doi-asserted-by":"publisher","first-page":"bbac505","DOI":"10.1093\/bib\/bbac505","article-title":"Virus classification for viral genomic fragments using PhaGCN2","volume":"24","author":"Jiang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"B70","doi-asserted-by":"publisher","first-page":"vbaf188","DOI":"10.1093\/bioadv\/vbaf188","article-title":"ProkBERT PhaStyle: accurate phage lifestyle prediction with pretrained genomic language models","volume":"5","author":"Juhasz","year":"2025","journal-title":"Bioinform. Adv."},{"key":"B71","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/s00108-023-01620-z","article-title":"Precision medicine in infectious diseases","volume":"65","author":"Jung","year":"2024","journal-title":"Inn. Med."},{"key":"B72","doi-asserted-by":"publisher","first-page":"e2313574121","DOI":"10.1073\/pnas.2313574121","article-title":"Predictive phage therapy for Escherichia coli urinary tract infections: cocktail selection for therapy based on machine learning models","volume":"121","author":"Keith","year":"2024","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"B73","doi-asserted-by":"publisher","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":"B74","doi-asserted-by":"publisher","first-page":"D340","DOI":"10.1093\/nar\/gkae1119","article-title":"BFVD-a large repository of predicted viral protein structures","volume":"53","author":"Kim","year":"","journal-title":"Nucleic Acids Res."},{"key":"B75","doi-asserted-by":"publisher","first-page":"1468686","DOI":"10.3389\/fmicb.2024.1468686","article-title":"Targeted dual-receptor phage cocktail against Cronobacter sakazakii: insights into phage-host interactions and resistance mechanisms","volume":"15","author":"Kim","year":"","journal-title":"Front. Microbiol."},{"key":"B76","doi-asserted-by":"publisher","first-page":"eadv0870","DOI":"10.1126\/sciadv.adv0870","article-title":"RBPseg: toward a complete phage tail fiber structure atlas","volume":"11","author":"Klein-Sousa","year":"2025","journal-title":"Sci. Adv."},{"key":"B77","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/nrmicro2315","article-title":"Bacteriophage resistance mechanisms","volume":"8","author":"Labrie","year":"2010","journal-title":"Nat. Rev. Microbiol."},{"key":"B78","doi-asserted-by":"publisher","author":"Langenhuijsen","year":"","DOI":"10.2139\/ssrn.4333138"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2023.04.012","article-title":"Systematic metareview of prediction studies demonstrates stable trends in bias and low PROBAST inter-rater agreement","author":"Langenhuijsen","year":"","journal-title":"J. Clin. Epidemiol"},{"key":"B80","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1016\/j.csbj.2021.12.032","article-title":"Predicting the capsid architecture of phages from metagenomic data","volume":"20","author":"Lee","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B81","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1186\/s12859-018-2388-7","article-title":"Computational prediction of inter-species relationships through omics data analysis and machine learning","volume":"19","author":"Leite","year":"2018","journal-title":"BMC Bioinform"},{"key":"B82","doi-asserted-by":"publisher","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":"B83","doi-asserted-by":"publisher","first-page":"bbab348","DOI":"10.1093\/bib\/bbab348","article-title":"PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion","volume":"23","author":"Li","year":"2022","journal-title":"Brief. Bioinform."},{"key":"B84","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1038\/s41551-022-00942-x","article-title":"Graph representation learning in biomedicine and healthcare","volume":"6","author":"Li","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"B85","doi-asserted-by":"publisher","first-page":"btad259","DOI":"10.1093\/bioinformatics\/btad259","article-title":"AcrNET: predicting anti-CRISPR with deep learning","volume":"39","author":"Li","year":"2023","journal-title":"Bioinformatics"},{"key":"B86","doi-asserted-by":"publisher","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":"2025","journal-title":"Brief. Bioinform."},{"key":"B87","doi-asserted-by":"publisher","first-page":"177529","DOI":"10.1109\/ACCESS.2019.2957833","article-title":"VFM: identification of bacteriophages from metagenomic bins and contigs based on features related to gene and genome composition","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"B88","doi-asserted-by":"publisher","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":"B89","doi-asserted-by":"publisher","first-page":"932661","DOI":"10.3389\/fmicb.2022.932661","article-title":"Ensemble learning-based feature selection for phage protein prediction","volume":"13","author":"Liu","year":"2022","journal-title":"Front. Microbiol."},{"key":"B90","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12915-020-00938-6","article-title":"Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics","volume":"19","author":"Lu","year":"2021","journal-title":"BMC Biol"},{"key":"B91","doi-asserted-by":"publisher","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":"Lucia-Sanz","year":"2024","journal-title":"Virus Evol."},{"key":"B92","doi-asserted-by":"publisher","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":"B93","doi-asserted-by":"publisher","first-page":"bbaf656","DOI":"10.1093\/bib\/bbaf656","article-title":"PBIP: a deep learning framework for predicting phage\u2013bacterium interactions at the strain level","volume":"26","author":"Ma","year":"","journal-title":"Brief. Bioinform."},{"key":"B94","doi-asserted-by":"publisher","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":"","journal-title":"Knowl. Based Syst."},{"key":"B95","doi-asserted-by":"publisher","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":"","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"B96","doi-asserted-by":"publisher","first-page":"530424","DOI":"10.1186\/s12859-023-05341-w","article-title":"DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases","volume":"24","author":"Magill","year":"2023","journal-title":"BMC Bioinform."},{"key":"B97","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3389\/fmicb.2018.00476","article-title":"PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front. Microbiol."},{"key":"B98","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1093\/bioinformatics\/bts014","article-title":"PHACTS, a computational approach to classifying the lifestyle of phages","volume":"28","author":"McNair","year":"2012","journal-title":"Bioinformatics"},{"key":"B99","doi-asserted-by":"publisher","first-page":"bbae377","DOI":"10.1093\/bib\/bbae377","article-title":"DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information","volume":"25","author":"Miao","year":"2024","journal-title":"Brief. Bioinform."},{"key":"B100","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1093\/bib\/bbw068","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief. Bioinform."},{"key":"B101","doi-asserted-by":"publisher","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"B102","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"B103","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.3390\/life15091417","article-title":"Prophage activation: an in silico platform for identifying prophage regulatory elements to inform phage engineering against drug-resistant bacteria","volume":"15","author":"Musrrat","year":"2025","journal-title":"Life"},{"key":"B104","doi-asserted-by":"publisher","first-page":"104121","DOI":"10.1016\/j.isci.2022.104121","article-title":"A phage foundry framework to systematically develop viral countermeasures to combat antibiotic-resistant bacterial pathogens","volume":"25","author":"Mutalik","year":"2022","journal-title":"iScience"},{"key":"B105","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1016\/S0140-6736(24)01867-1","article-title":"Global burden of bacterial antimicrobial resistance 1990\u20132021: a systematic analysis with forecasts to 2050","volume":"404","author":"Naghavi","year":"2024","journal-title":"Lancet"},{"key":"B106","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1016\/j.cmi.2023.01.021","article-title":"Pharmacokinetics\/pharmacodynamics of phage therapy: a major hurdle to clinical translation","volume":"29","author":"Nang","year":"2023","journal-title":"Clin. Microbiol. Infect."},{"key":"B107","doi-asserted-by":"publisher","first-page":"n2281","DOI":"10.1136\/bmj.n2281","article-title":"Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review","volume":"375","author":"Navarro","year":"2021","journal-title":"BMJ"},{"key":"B108","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1080\/03009734.2019.1688433","article-title":"Pharmacological limitations of phage therapy","volume":"124","author":"Nilsson","year":"2019","journal-title":"Ups. J. Med. Sci."},{"key":"B109","doi-asserted-by":"publisher","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":"B110","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"B111","doi-asserted-by":"publisher","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":"B112","doi-asserted-by":"publisher","first-page":"111647","DOI":"10.1016\/j.isci.2024.111647","article-title":"Predicting phage-host interaction via hyperbolic Poincar\u00e9 graph embedding and large-scale protein language technique","volume":"28","author":"Pan","year":"2025","journal-title":"iScience"},{"key":"B113","doi-asserted-by":"publisher","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":"","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B114","doi-asserted-by":"publisher","first-page":"bbad328","DOI":"10.1093\/bib\/bbad328","article-title":"PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network","volume":"24","author":"Pan","year":"","journal-title":"Brief. Bioinform."},{"key":"B115","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.3390\/ijms19061779","article-title":"Identification of bacteriophage virion proteins using multinomial Na\u00efve Bayes with g-gap feature tree","volume":"19","author":"Pan","year":"2018","journal-title":"Int. J. Mol. Sci."},{"key":"B116","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s11095-014-1617-7","article-title":"Quality and safety requirements for sustainable phage therapy products","volume":"32","author":"Pirnay","year":"2015","journal-title":"Pharm. Res."},{"key":"B117","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1038\/s41564-024-01705-x","article-title":"Personalized bacteriophage therapy outcomes for 100 consecutive cases: a multicentre, multinational, retrospective observational study","volume":"9","author":"Pirnay","year":"2024","journal-title":"Nat. Microbiol."},{"key":"B118","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3390\/v10020064","article-title":"The Magistral Phage","volume":"10","author":"Pirnay","year":"2018","journal-title":"Viruses"},{"key":"B119","doi-asserted-by":"publisher","first-page":"e954","DOI":"10.1002\/cpz1.954","article-title":"Determination of optimal phage load and administration time for antibacterial treatment","volume":"4","author":"Plunder","year":"2024","journal-title":"Curr. Protoc."},{"key":"B120","doi-asserted-by":"publisher","first-page":"1040457","DOI":"10.3389\/fmed.2022.1040457","article-title":"Determination of phage load and administration time in simulated occurrences of antibacterial treatments","volume":"9","author":"Plunder","year":"2022","journal-title":"Front. Med."},{"key":"B121","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac468","article-title":"3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs","author":"Pu","year":"2022","journal-title":"Bioinformatics"},{"key":"B122","doi-asserted-by":"publisher","first-page":"bbae010","DOI":"10.1093\/bib\/bbae010","article-title":"PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy","volume":"25","author":"Qiu","year":"2024","journal-title":"Brief. Bioinform."},{"key":"B123","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1089\/phage.2023.0043","article-title":"Between centralization and fragmentation: the past, present, and future of phage collections","volume":"5","author":"Resch","year":"2024","journal-title":"PHAGE"},{"key":"B124","doi-asserted-by":"publisher","first-page":"e3002083","DOI":"10.1371\/journal.pbio.3002083","article-title":"iPHoP: an integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria","volume":"21","author":"Roux","year":"2023","journal-title":"PLoS Biol."},{"key":"B125","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3389\/fmicb.2019.00507","article-title":"Identification of phage viral proteins with hybrid sequence features","volume":"10","author":"Ru","year":"2019","journal-title":"Front. Microbiol."},{"key":"B126","doi-asserted-by":"publisher","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":"B127","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1038\/nrmicro3564","article-title":"A century of the phage: past, present and future","volume":"13","author":"Salmond","year":"2015","journal-title":"Nat. Rev. Microbiol."},{"key":"B128","doi-asserted-by":"publisher","first-page":"5301","DOI":"10.1093\/bioinformatics\/btz580","article-title":"Predicting promoters in phage genomes using PhagePromoter","volume":"35","author":"Sampaio","year":"2019","journal-title":"Bioinformatics"},{"key":"B129","doi-asserted-by":"publisher","first-page":"e00954","DOI":"10.1128\/AAC.00954-17","article-title":"Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii Infection","volume":"61","author":"Schooley","year":"2017","journal-title":"Antimicrob. Agents Chemother."},{"key":"B130","doi-asserted-by":"publisher","first-page":"e1002657","DOI":"10.1371\/journal.pcbi.1002657","article-title":"Artificial neural networks trained to detect viral and phage structural proteins","volume":"8","author":"Seguritan","year":"2012","journal-title":"PLoS Comput. Biol."},{"key":"B131","doi-asserted-by":"publisher","first-page":"vbad101","DOI":"10.1093\/bioadv\/vbad101","article-title":"PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data","volume":"3","author":"Shang","year":"","journal-title":"Bioinform. Adv."},{"key":"B132","doi-asserted-by":"publisher","first-page":"I30","DOI":"10.1093\/bioinformatics\/btad229","article-title":"PhaVIP: phage virion protein classification based on chaos game representation and vision transformer","volume":"39","author":"Shang","year":"","journal-title":"Bioinformatics"},{"key":"B133","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1186\/s12915-021-01180-4","article-title":"Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning","volume":"19","author":"Shang","year":"2021","journal-title":"BMC Biol"},{"key":"B134","doi-asserted-by":"publisher","first-page":"bbac182","DOI":"10.1093\/bib\/bbac182","article-title":"CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model","volume":"23","author":"Shang","year":"2022","journal-title":"Brief. Bioinform."},{"key":"B135","doi-asserted-by":"publisher","first-page":"bbac258","DOI":"10.1093\/bib\/bbac258","article-title":"Accurate identification of bacteriophages from metagenomic data using transformer","volume":"23","author":"Shang","year":"2022","journal-title":"Brief. Bioinform."},{"key":"B136","doi-asserted-by":"publisher","first-page":"bbac487","DOI":"10.1093\/bib\/bbac487","article-title":"PhaTYP: predicting the lifestyle for bacteriophages using BERT","volume":"24","author":"Shang","year":"","journal-title":"Brief. Bioinform."},{"key":"B137","doi-asserted-by":"publisher","first-page":"9392","DOI":"10.1038\/s41467-024-53759-4","article-title":"A long-context language model for deciphering and generating bacteriophage genomes","volume":"15","author":"Shao","year":"2024","journal-title":"Nat. Commun."},{"key":"B138","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.jtbi.2017.09.022","article-title":"iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features","volume":"435","author":"Shatabda","year":"2017","journal-title":"J. Theor. Biol."},{"key":"B139","doi-asserted-by":"publisher","first-page":"1061122","DOI":"10.3389\/fmicb.2022.1061122","article-title":"iProm-phage: a two-layer model to identify phage promoters and their types using a convolutional neural network","volume":"13","author":"Shujaat","year":"2022","journal-title":"Front. Microbiol."},{"key":"B140","doi-asserted-by":"publisher","first-page":"lqaa109","DOI":"10.1093\/nargab\/lqaa109","article-title":"Rapid discovery of novel prophages using biological feature engineering and machine learning","volume":"3","author":"Siren","year":"2021","journal-title":"NAR Genom. Bioinform."},{"key":"B141","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/s41746-025-01725-9","article-title":"Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap","volume":"8","author":"Sokol","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"B142","doi-asserted-by":"publisher","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":"B143","doi-asserted-by":"publisher","first-page":"e02071","DOI":"10.1128\/aac.02071-21","article-title":"Considerations for the use of phage therapy in clinical practice","volume":"66","author":"Suh","year":"2022","journal-title":"Antimicrob. Agents Chemother."},{"key":"B144","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1093\/bioinformatics\/btab585","article-title":"HoPhage: an ab initio tool for identifying hosts of phage fragments from metaviromes","volume":"38","author":"Tan","year":"2022","journal-title":"Bioinformatics"},{"key":"B145","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.3390\/molecules23082000","article-title":"Identifying phage virion proteins by using two-step feature selection methods","volume":"23","author":"Tan","year":"2018","journal-title":"Molecules"},{"key":"B146","doi-asserted-by":"publisher","first-page":"i45","DOI":"10.1093\/bioinformatics\/btac239","article-title":"Phage\u2013bacterial contig association prediction with a convolutional neural network","volume":"38","author":"Tang","year":"2022","journal-title":"Bioinformatics"},{"key":"B147","unstructured":"2023"},{"key":"B148","doi-asserted-by":"publisher","first-page":"e00242","DOI":"10.1128\/mSystems.00242-21","article-title":"Component parts of bacteriophage virions accurately defined by a machine-learning approach built on evolutionary features","volume":"6","author":"Thung","year":"2021","journal-title":"mSystems"},{"key":"B149","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1089\/phage.2023.0045","article-title":"Addressing the research and development gaps in modern phage therapy","volume":"5","author":"Turner","year":"2024","journal-title":"Phage Ther. Appl. Res."},{"key":"B150","doi-asserted-by":"publisher","first-page":"bbae640","DOI":"10.1093\/bib\/bbae640","article-title":"Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks","volume":"26","author":"Ultsch","year":"2024","journal-title":"Brief. Bioinform."},{"key":"B151","doi-asserted-by":"publisher","first-page":"23040","DOI":"10.1038\/s41598-023-49880-x","article-title":"Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation","volume":"13","author":"Unnikrishnan","year":"2023","journal-title":"Sci. Rep"},{"key":"B152","doi-asserted-by":"publisher","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":"B153","doi-asserted-by":"publisher","first-page":"109836","DOI":"10.1016\/j.compbiomed.2025.109836","article-title":"PhageDPO: a machine-learning based computational framework for identifying phage depolymerases","volume":"188","author":"Vieira","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"B154","doi-asserted-by":"publisher","first-page":"lqae185","DOI":"10.1093\/nargab\/lqae185","article-title":"vClean: assessing virus sequence contamination in viral genomes","volume":"7","author":"Wagatsuma","year":"2025","journal-title":"NAR Genom. Bioinform."},{"key":"B155","doi-asserted-by":"publisher","first-page":"e1004264","DOI":"10.1371\/journal.pmed.1004264","article-title":"Antimicrobial resistance: addressing a global threat to humanity","volume":"20","author":"Walsh","year":"2023","journal-title":"PLoS Med."},{"key":"B156","doi-asserted-by":"publisher","first-page":"5258","DOI":"10.1109\/JBHI.2022.3193224","article-title":"DPProm: a two-layer predictor for identifying promoters and their types on phage genome using deep learning","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"B157","doi-asserted-by":"publisher","first-page":"W348","DOI":"10.1093\/nar\/gkaa432","article-title":"PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B158","doi-asserted-by":"publisher","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":"B159","doi-asserted-by":"publisher","first-page":"D756","DOI":"10.1093\/nar\/gkad979","article-title":"PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations","volume":"52","author":"Wang","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B160","doi-asserted-by":"publisher","first-page":"3061","DOI":"10.1109\/JBHI.2023.3261319","article-title":"An effective model for predicting phage-host interactions via graph embedding representation learning with multi-head attention mechanism","volume":"27","author":"Wang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"B161","doi-asserted-by":"publisher","first-page":"6092","DOI":"10.1109\/JBHI.2025.3548463","article-title":"Advanced heterogeneous network-based graph neural network framework for predicting anti-CRISPR protein sequences","volume":"29","author":"Wang","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"B162","doi-asserted-by":"publisher","first-page":"bbae672","DOI":"10.1093\/bib\/bbae672","article-title":"Predicting phage\u2013host interactions via feature augmentation and regional graph convolution","volume":"26","author":"Wei","year":"","journal-title":"Brief. Bioinform."},{"key":"B163","doi-asserted-by":"publisher","first-page":"bbae708","DOI":"10.1093\/bib\/bbae708","article-title":"A novel framework for phage-host prediction via logical probability theory and network sparsification","volume":"26","author":"Wei","year":"","journal-title":"Brief. Bioinform."},{"key":"B164","doi-asserted-by":"publisher","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":"B165","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med"},{"key":"B166","doi-asserted-by":"publisher","first-page":"btad120","DOI":"10.1093\/bioinformatics\/btad120","article-title":"PhaGAA: an integrated web server platform for phage genome annotation and analysis","volume":"39","author":"Wu","year":"2023","journal-title":"Bioinformatics"},{"key":"B167","doi-asserted-by":"publisher","first-page":"giab056","DOI":"10.1093\/gigascience\/giab056","article-title":"DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach","volume":"10","author":"Wu","year":"2021","journal-title":"Gigascience"},{"key":"B168","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1109\/JBHI.2024.3500137","article-title":"A novel framework for predicting phage-host interactions via host specificity-aware graph autoencoder","volume":"29","author":"Xiao","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"B169","doi-asserted-by":"publisher","first-page":"btad309","DOI":"10.1093\/bioinformatics\/btad309","article-title":"Genome mining for anti-CRISPR operons using machine learning","volume":"39","author":"Yang","year":"2023","journal-title":"Bioinformatics"},{"key":"B170","doi-asserted-by":"publisher","first-page":"giae017","DOI":"10.1093\/gigascience\/giae017","article-title":"Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants","volume":"13","author":"Yang","year":"2024","journal-title":"Gigascience"},{"key":"B171","doi-asserted-by":"publisher","first-page":"bbab340","DOI":"10.1093\/bib\/bbab340","article-title":"Graph representation learning in bioinformatics: trends, methods and applications","volume":"23","author":"Yi","year":"2022","journal-title":"Brief. Bioinform."},{"key":"B172","doi-asserted-by":"publisher","first-page":"4942","DOI":"10.1016\/j.csbj.2022.08.053","article-title":"IBPred: a sequence-based predictor for identifying ion binding protein in phage","volume":"20","author":"Yuan","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B173","doi-asserted-by":"publisher","first-page":"342","DOI":"10.3390\/v14020342","article-title":"PhageLeads: rapid assessment of phage therapeutic suitability using an ensemble machine learning approach","volume":"14","author":"Yukgehnaish","year":"2022","journal-title":"Viruses"},{"key":"B174","doi-asserted-by":"publisher","first-page":"21734","DOI":"10.3390\/ijms160921734","article-title":"An ensemble method to distinguish bacteriophage virion from non-virion proteins based on protein sequence characteristics","volume":"16","author":"Zhang","year":"2015","journal-title":"Int. J. Mol. Sci."},{"key":"B175","doi-asserted-by":"publisher","first-page":"e2404049","DOI":"10.1002\/advs.202404049","article-title":"Discovery of antimicrobial lysins from the \u201cDark Matter\u201d of uncharacterized phages using artificial intelligence","volume":"11","author":"Zhang","year":"","journal-title":"Adv. Sci."},{"key":"B176","doi-asserted-by":"publisher","first-page":"1012525","DOI":"10.1371\/journal.pcbi.1012525","article-title":"DeepPL: a deep-learning-based tool for the prediction of bacteriophage lifecycle","volume":"20","author":"Zhang","year":"","journal-title":"PLoS Comput. Biol."},{"key":"B177","doi-asserted-by":"publisher","first-page":"bbad239","DOI":"10.1093\/bib\/bbad239","article-title":"Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning","volume":"24","author":"Zhang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"B178","doi-asserted-by":"publisher","first-page":"giae074","DOI":"10.1093\/gigascience\/giae074","article-title":"PhageGE: an interactive web platform for exploratory analysis and visualization of bacteriophage genomes","volume":"13","author":"Zhao","year":"2024","journal-title":"Gigascience"},{"key":"B179","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s40168-019-0657-y","article-title":"Mining, analyzing, and integrating viral signals from metagenomic data","volume":"7","author":"Zheng","year":"2019","journal-title":"Microbiome"},{"key":"B180","doi-asserted-by":"publisher","first-page":"947466","DOI":"10.3389\/fgene.2023.947466","article-title":"PhageTailFinder: a tool for phage tail module detection and annotation","volume":"14","author":"Zhou","year":"2023","journal-title":"Front. Genet."},{"key":"B181","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1186\/s12859-022-04986-3","article-title":"PreAcrs: a machine learning framework for identifying anti-CRISPR proteins","volume":"23","author":"Zhu","year":"2022","journal-title":"BMC Bioinform."}],"container-title":["Frontiers in Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2026.1873220\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:40:18Z","timestamp":1788972018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2026.1873220\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":181,"alternative-id":["10.3389\/fmicb.2026.1873220"],"URL":"https:\/\/doi.org\/10.3389\/fmicb.2026.1873220","relation":{},"ISSN":["1664-302X"],"issn-type":[{"value":"1664-302X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"article-number":"1873220"}}