ReviewBMC microbiology2021
Application of machine learning in bacteriophage research.
Review in BMC microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 60 citations in OpenAlex.
- Phage Genomics and Bioinformatics in Therapeutic Development: Evidence Limits, Validation Gates, and Translational Decision-Making.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Synergistic potential of probiotics and bacteriophages in combating multidrug-resistant microbial infections: A novel therapeutic strategy for the post-antibiotic era.New microbes and new infections · 2026Review
- Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.Journal of food science · 2026Review
- Comparison and correlation of in vitro and in vivo approaches for determining Pseudomonas aeruginosa bacteriophages activity.BMC microbiology · 2026Article
- Bacteriophage therapy beyond antibiotics: emerging innovations for infectious and non-infectious diseases.Frontiers in cellular and infection microbiology · 2026Review
- The Emerging Role of Dietary Bacteriophage in Monogastric Animals in the Post-Antibiotic Era-A Review.Veterinary sciences · 2025Review
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
- Unlocking data in Klebsiella lysogens to predict capsular type-specificity of phage depolymerases.Nature communications · 2025Article
- Dual Nature of Bacteriophages: Friends or Foes in Minimally Processed Food Products-A Comprehensive Review.Viruses · 2025Review
- Regulatory Landscape and the Potential of Bacteriophage Applications in the United States' Food Industry.Journal of food protection · 2025Review
- Bridging the gap: Phage manufacturing processes from laboratory to agri-food industry.Virus research · 2025Review
- Review
- Deep learning-guided structural analysis of a novel bacteriophage KPP105 against multidrug-resistantComputational and structural biotechnology journal · 2025Article
- Optimizing phage therapy with artificial intelligence: a perspective.Frontiers in cellular and infection microbiology · 2025Review
- Leveraging machine learning and big data techniques to map the global patent landscape of phage therapy.Nature biotechnology · 2024Article
- Beyond Antibiotics: Exploring the Potential of Bacteriophages and Phage Therapy.PHAGE (New Rochelle, N.Y.) · 2024Review
- Article
- Predictive phage therapy forProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Occurrence and diversity pattern of CRISPR-Cas systems inFrontiers in microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.