机器学习方法在代谢组学数据统计分析中的新近研究进展

doi:10.3969/j.issn.1006-5253.2024.04.015

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Abstract Metabolomics is a disciplinary field that investigates all small-molecule metabolites within the biological organism. Utilizing advanced technological platforms such as mass spectrometry and nuclear magnetic resonance, comprehensive and high-throughput detection of metabolites can offer new avenues for early disease diagnosis, elucidation of pathogenic mechanisms, and personalized therapeutic approaches. Due to the diversity, high dimensionality, dynamics, noise, and variability inherent in metabolomics data, it poses formidable challenges for the research and development of data analysis methods. This paper aims to provide a comprehensive overview of recent advancements of machine learning methods for statistical analysis on metabolomics data, with particular focus on improved approaches. By synthesizing these methodologies, the intention is to offer a crucial foundation for effective data analysis and to fully harness the potential of metabolomics in medical research.

Key words： metabolomics medical research machine learning method

Received: 21 March 2024

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	Articles by authors
	Hao Panfeng
	Pan Shuze
	Qin Linyuan
	Wu Xiaoyan

Cite this article:

Hao Panfeng,Pan Shuze,Qin Linyuan, et al. Recent development of machine learning methods applied on statistical analysis for metabolomics data[J]. journal1, 2024, 31(4): 313-320.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2024.04.015 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2024/V31/I4/313

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