AI让微生物研究更精准，从分类到治疗全链条赋能
Artificial Intelligence for Microbiology and Microbiome Research
- 用机器学习与深度学习分析微生物数据，匹配研究目标选方法
- 覆盖分类、功能预测、生态互作等10大应用方向，提升研究效率
- 适合微生物学、临床医学及精准营养领域研究人员参考
人工智能的进展已深刻改变多个科学领域，微生物学与微生物组研究正借助机器学习实现重大突破。本文综述了专为微生物与微生物组研究设计的AI方法，涵盖基础技术如主流机器学习范式与各类深度学习架构，并根据研究目标提供传统机器学习与复杂深度学习的选择建议。核心部分系统阐述了多样化应用场景，包括分类学分析、功能注释与预测、微生物-宿主互作、微生物生态、代谢建模、精准营养、临床微生物学以及预防与治疗。最后讨论该领域的挑战并强调若干最新进展。本综述凸显了人工智能在微生物研究中的变革性作用，推动新型方法与应用的发展，深化我们对微生物生命及其对地球和健康影响的理解。
原文摘要 · Abstract (English)
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.
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