Metabolomics in the Era of Artificial Intelligence

in Microbiota and Host
Authors:
Elizabeth A Coler E Coler, Department of Chemistry, University of Connecticut, Storrs, 06269, United States

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Wunxuan Chen W Chen, Department of Chemistry, University of Connecticut, Storrs, United States

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Alexey V Melnik A Melnik, Department of Chemistry, University of Connecticut, Storrs, United States

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James T Morton J Morton, Gutz Analytics LLC, Boulder, United States

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Alexander A Aksenov A Aksenov, Department of Chemistry, University of Connecticut, Storrs, United States

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Correspondence: Alexander Aksenov, Email: aaksenov@uconn.edu
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Artificial Intelligence (AI) is rapidly revolutionizing our daily lives, as it automates mundane tasks, enhances productivity, and transforms how we interact with technology. We believe it is inevitable that AI will soon become a crucial tool in common research practices, from data analysis to writing papers. Here we explore how this transition is occurring in the field of mass spectrometry-based metabolomics, a rapidly growing area of science. Metabolomics focuses on studying small molecules in biological systems, offering valuable insights into metabolic processes and their impact on health, diseases, and physiological conditions. With the remarkable advancements in sequencing technologies and the exploration of the microbiome, the combination of sequencing and metabolomics presents profound opportunities to understand biological complexity. Incorporating AI will unlock new possibilities and will, in all likelihood, contribute to scientific discoveries in the future. In this review we discuss the current role of AI in metabolomics. Existing practices are examined and we also provide a perspective on future directions for integrating AI into scientific research.

 

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