Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.

Area of Science:

• Microbiome research and computational biology.

Background:

• Gut microbiota play crucial roles in human health and disease.
• High-throughput technologies generate vast amounts of microbial data.

Purpose of the Study:

• To review the relationship between gut microbiota dysbiosis and human diseases.
• To explore machine learning applications in analyzing gut microbiome data.
• To discuss the potential of machine learning in developing targeted therapies.

Main Methods:

• Analysis of existing literature on gut microbiota and machine learning.
• Categorization and workflow explanation of machine learning approaches.
• Review of multi-omics data integration using machine learning.

Main Results:

• Gut microbiota dysbiosis is linked to various human diseases.
• Machine learning algorithms effectively identify molecular signatures in microbiome data.
• Machine learning facilitates integrative analysis of multi-omics data.

Conclusions:

• Machine learning is highly suitable for gut microbiome analysis.
• Machine learning approaches can guide the development of gut microbe-targeted therapies.
• These methods contribute to personalized and precision medicine.