Artificial Intelligence for Microbiology and Microbiome Research

TL;DR

This review highlights how artificial intelligence, especially machine learning and deep learning, is revolutionizing microbiology and microbiome research by enabling new insights, applications, and overcoming existing challenges.

Contribution

It provides a comprehensive overview of AI techniques and their applications in microbiology and microbiome studies, emphasizing recent breakthroughs and future directions.

Findings

AI enhances taxonomic profiling and functional annotation.

Deep learning models improve microbial interaction predictions.

Recent breakthroughs enable personalized microbiome-based therapies.

Abstract

Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, and offer guidance on choosing between traditional machine learning and sophisticated deep learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas, from taxonomic profiling, functional annotation \& prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention \& therapeutics. Finally, we discuss challenges in this field and highlight some recent breakthroughs. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies and applications that enhance our understanding of microbial life and its impact on our planet and our health.