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Regular follow-up appointments allow for monitoring of tolerability, efficacy, and early identification of complications requiring intervention

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AI in metabolomics can be used in various aspects of metabolomic analysis, and significantly improve the efficiency and accuracy of metabolomic studies, including analytical detection, feature selection and data extraction and pre-processing, biomarker discovery, interpretation of biochemical pathways, as well as integration with other multi-omics data and predictive modelling for personalized approach (Chi et al

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