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Updated: Feb 28, 2026

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人工智能用于研究精密医学的代谢学数据

Antony Shenouda¹, Sahana Senthilkumar¹, Youssef Mourad¹

¹Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, 08901, USA.

Metabolomics : Official journal of the Metabolomic Society

|February 27, 2026

概括

人工智能 (AI) 和机器学习 (ML) 显著提升了疾病洞察力的代谢学. 这些AI/ML方法有助于生物标志物发现和疾病预测,这对精准医学至关重要.

科学领域:

代谢学和生物信息学
医疗保健中的人工智能
精准医学是一门精准的医学.

背景情况:

代谢数据分析是了解疾病机制和确定治疗点的关键.
传统的方法与高维度,非线性代谢数据作斗争.
人工智能和机器学习为代谢数据分析提供了更高的灵敏度和适应性.

研究的目的:

在代谢学研究中分析和比较AI/ML方法.
评估AI/ML在精准医学中的影响.
审查对代谢数据的AI/ML应用的进展.

主要方法:

对同行评审研究进行系统审查,并在PubMed.
对AI/ML在癌症,心血管疾病和糖尿病等各种疾病中的应用进行分析.
科学目标,方法,数据集和AI/ML方法的来源的比较.

主要成果:

支持矢量机器 (SVM),随机森林 (RF),梯度提升和后勤回归是常用的AI/ML技术.
这些方法在处理复杂的代谢数据方面表现出有效性.
确定的挑战包括小队列规模,数据异质性和可解释性.

结论:

人工智能/ML对代谢学具有变革性的潜力.
通过预测性代谢分析,AI/ML对于推进精准医学至关重要.
未来的工作必须解决当前面临的挑战,以便在代谢学中更广泛地采用AI/ML.

关键词:

人工智能,机器学习,机器学习代谢学代谢学代谢学精准医学是一门精准的医学.它具有治疗性的作用.

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