AI-empowered human microbiome research

Area of Science:

• Microbiome Research
• Computational Biology
• Artificial Intelligence

Background:

• High-throughput microbiome profiling yields vast, complex datasets.
• Traditional computational methods struggle with high-dimensional microbiome data.
• Artificial intelligence (AI) offers powerful solutions for analyzing microbial data.

Purpose of the Study:

• To systematically review AI-driven methodologies in microbiome research.
• To assess AI's capability in extracting biological patterns from microbial data.
• To explore AI's impact on translational applications in health and medicine.

Main Methods:

• Review of AI methodologies including machine learning and deep learning.
• Exploration of clustering algorithms, dimensionality reduction, neural networks, and large language models.
• Assessment of AI applications in analyzing community dynamics, host-microbe interactions, and functional genomics.

Main Results:

• AI effectively extracts meaningful patterns from complex microbiome data.
• AI facilitates insights into microbial community dynamics and host-microbe interactions.
• AI demonstrates transformative potential in diagnostics, therapeutics, and microbiome engineering.

Conclusions:

• AI is crucial for overcoming analytical challenges in microbiome research.
• AI integration accelerates personalized medicine and understanding of host-microbiome relationships.
• Critical evaluation of AI capabilities and limitations is essential for future integration.