Application of machine learning in bacteriophage research.

Area of Science:

• Microbiology and Bioinformatics
• Computational Biology and Machine Learning

Background:

• Bacteriophages (phages) are crucial components of microbial communities, impacting human health and the food industry.
• Traditional in vitro methods for phage characterization are time-consuming, costly, and labor-intensive.
• High-throughput sequencing necessitates advanced computational frameworks for analyzing newly identified bacteriophages.

Purpose of the Study:

• To conduct a comprehensive review of machine learning (ML) methods applied to bacteriophage research.
• To explore the application of ML across various phage research aspects, including identification, classification, and host recognition.
• To discuss the advantages and limitations of ML-based computational frameworks in phage characterization.

Main Methods:

• Systematic review of existing literature on machine learning applications in bacteriophage research.
• Analysis of different feature types used in ML models for phage characterization.
• Categorization of ML applications into automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction.

Main Results:

• Machine learning methods are increasingly applied to diverse bacteriophage research areas, offering efficient alternatives to traditional methods.
• Various ML techniques, utilizing different feature sets, have shown promise in automated phage identification, classification, and host prediction.
• The review highlights the potential of ML in accelerating knowledge discovery and pattern recognition within complex phage datasets.

Conclusions:

• Machine learning provides powerful computational tools essential for characterizing newly identified bacteriophages, driven by advances in sequencing technology.
• ML applications offer significant advantages in speed and efficiency for tasks like phage identification, classification, and host prediction.
• Further development and discussion of ML frameworks are crucial for advancing bacteriophage research and understanding their roles in microbial ecosystems.