Chi Jinhua, Shu Jingmin, Li Ming, Mudappathi Rekha, Jin Yan, Lewis Freeman, Boon Alexandria, Qin Xiaoyan, Liu Li, Gu Haiwei
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA.
Trends Analyt Chem. 2024 Sep;178. doi: 10.1016/j.trac.2024.117852. Epub 2024 Jul 3.
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
代谢组学与人工智能（AI）形成了一种协同合作关系。代谢组学产生包含数百至数千种具有复杂关系的代谢物的大型数据集。旨在通过计算建模模拟人类智能的人工智能，在大数据分析方面具有非凡的能力。在本综述中，我们在系统生物学和人类健康的背景下，对人工智能在代谢组学研究中的方法和应用进行了最新概述。我们首先介绍人工智能的概念、历史以及机器学习和深度学习的关键算法，总结它们的优缺点。然后，我们讨论了在代谢组学分析的不同方面成功使用人工智能的研究，包括分析检测、数据预处理、生物标志物发现、预测建模和多组学数据整合。最后，我们讨论了这个快速发展领域中存在的挑战和未来前景。尽管存在局限性和挑战，但代谢组学与人工智能的结合在促进人类健康方面取得革命性进展具有巨大潜力。
