Topic: Advances in AI-Driven Microbiome Research: Databases, Computational Resources and Models

The rapid expansion of microbiome research has generated vast and complex datasets encompassing microbial genomes, metagenomes, metabolomes, host–microbe interactions, and multi-omics profiles. Artificial intelligence (AI) and machine learning approaches are emerging as powerful tools to transform these large-scale biological datasets into meaningful insights, enabling the discovery of microbial functions, ecological patterns, disease-associated signatures, and novel therapeutic strategies. By integrating advanced computational methods with microbiome science, AI-driven approaches provide unprecedented opportunities to improve data interpretation, predictive modeling, and personalized microbiome-based interventions.

This Special Issue aims to highlight recent advances in AI-driven microbiome research, with a particular focus on the development of databases, computational resources, and innovative models for microbiome data analysis. Emphasis will be placed on artificial intelligence, machine learning, deep learning, and other computational frameworks that facilitate microbiome data integration, functional annotation, biomarker discovery, and prediction of host–microbiome interactions. By bringing together perspectives from microbiology, bioinformatics, artificial intelligence, systems biology, and computational medicine, this Special Issue seeks to promote interdisciplinary approaches that accelerate microbiome discovery and advance the application of AI technologies in microbiome research.

Topics of interest include, but are not limited to:

● AI and machine learning approaches for microbiome data analysis and interpretation;

● Development of microbiome databases, repositories, and computational resources;

● Deep learning models for microbial identification, functional prediction, and community profiling;

● AI-driven discovery of microbiome biomarkers for health and disease;

● Computational modeling of host–microbiome interactions and microbial ecosystems;

● Multi-omics data integration using artificial intelligence and systems biology approaches;

● Predictive models for microbiome-based diagnostics and personalized medicine;

● Large language models and generative AI applications in microbiome research;

● AI-assisted analysis of microbial genomes, metagenomes, and metabolomic datasets.

Artificial intelligence, machine learning, deep learning, microbiome, microbiome databases, computational resources, multi-omics, host–microbiome interactions