Application of Machine Learning in Microbiology.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microorganisms are vital to daily life and have been studied since the 19th century.
• Traditional cultivation methods for studying microbes are costly and slow.
• High-throughput sequencing necessitates advanced analytical approaches.

Purpose of the Study:

• To review the application of machine learning (ML) in the field of microbiology.
• To highlight ML's role in addressing challenges posed by traditional microbiological techniques.
• To summarize ML's utility in microbial classification and environmental interaction analysis.

Main Methods:

• Literature review of machine learning applications in microbiology.
• Analysis of ML's role in microbial classification.
• Exploration of ML's use in understanding microbe-environment interactions.

Main Results:

• Machine learning methods are increasingly adopted in microbiology.
• ML excels in microbial classification tasks.
• ML aids in deciphering complex interactions between microorganisms and their environments.

Conclusions:

• Machine learning provides powerful tools to advance microbiology research.
• ML overcomes the limitations of traditional cultivation methods.
• ML is essential for modern microbial studies, especially with high-throughput data.