Harnessing machine learning for metagenomic data analysis: trends and applications

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Metagenomic sequencing provides high-resolution microbial profiles but generates high-dimensional, sparse, and noisy data.
• Analyzing complex metagenomic datasets presents significant downstream data analysis challenges.

Purpose of the Study:

• To review current machine learning (ML) applications in metagenomic data analysis.
• To discuss challenges, model interpretability, and compare ML with mechanistic models.
• To preview future directions in AI and ML for microbiome science.

Main Methods:

• Survey of supervised and unsupervised learning, time-series modeling, transfer learning, causal ML, and generative models.
• Discussion of model interpretability and explainable AI (XAI).
• Comparative analysis of ML and mechanistic models.

Main Results:

• ML provides a robust toolkit for extracting insights from large, complex metagenomic datasets.
• Key challenges include data complexity and the need for model interpretability (XAI).
• ML and mechanistic models offer complementary strengths for microbiome research.

Conclusions:

• AI and ML are crucial for advancing microbiome science.
• Future directions include multi-omics integration, synthetic data generation, and Agentic AI.
• Synergies between ML and mechanistic approaches will drive future discoveries.