Machine learning and statistical inference in microbial population genomics

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Large genome datasets are transforming microbiology research.
• Computational analysis of these datasets is complex.
• Machine learning and statistical inference are key data analysis approaches.

Purpose of the Study:

• To review the distinct aims and methods of machine learning and statistical inference.
• To highlight their complementarity in microbial genomics.
• To advocate for their combined use in pathogen research.

Main Methods:

• Review of machine learning and statistical inference methodologies.
• Application examples from microbial genomics.
• Discussion of synthesis and complementarity.

Main Results:

• Machine learning excels at prediction.
• Statistical inference focuses on understanding relationships.
• Both fields share knowledge discovery goals.

Conclusions:

• Machine learning and statistical inference have different strengths but overlapping aims.
• Combining these approaches offers significant potential for big data-driven pathogen research.