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. 2024 Sep:178:117852.
doi: 10.1016/j.trac.2024.117852. Epub 2024 Jul 3.

Artificial Intelligence in Metabolomics: A Current Review

Jinhua Chi  1   2 , Jingmin Shu  1   3 , Ming Li  4   5 , Rekha Mudappathi  1   3 , Yan Jin  2 , Freeman Lewis  2 , Alexandria Boon  2 , Xiaoyan Qin  6 , Li Liu  1   3 , Haiwei Gu  1   2
Affiliations

Artificial Intelligence in Metabolomics: A Current Review

Jinhua Chi et al. Trends Analyt Chem. 2024 Sep.

Abstract

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.

Keywords: Artificial Intelligence; Deep Learning; Disease Diagnosis; Drug Discovery; Machine Learning; Metabolomics; Precision Medicine; Systems Biology.

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Conflict of interest statement

The authors declare no competing conflicts of interest. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1.
Figure 1.
History of artificial intelligence and its connections to metabolomics.
Figure 2.
Figure 2.
A general workflow in metabolomics.
Figure 3.
Figure 3.
Illustration of the four categories of feature selection strategies.
Figure 4.
Figure 4.
Systems biology: from genotype to phenotype in a biological system.
See this image and copyright information in PMC

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