{"id":2096,"date":"2026-02-27T16:42:11","date_gmt":"2026-02-27T16:42:11","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=2096"},"modified":"2026-02-28T15:42:57","modified_gmt":"2026-02-28T15:42:57","slug":"artificial-intelligence-to-investigate-metabolomics-data-for-precision-medicine","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/artificial-intelligence-to-investigate-metabolomics-data-for-precision-medicine\/","title":{"rendered":"Artificial intelligence to investigate metabolomics data for precision medicine"},"content":{"rendered":"

Our latest article,“Artificial intelligence to investigate metabolomics data for precision medicine<\/strong><\/a>“, published in Metabolomics<\/strong><\/a>.
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Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic targets by analyzing metabolic profiles. In analyzing these profiles, traditional bioinformatic and statistical approaches, while valuable, often struggle to process high-dimensional and nonlinear metabolic data, lacking the sensitivity and adaptability that artificial intelligence (AI) and machine learning (ML) techniques provide. The integration of AI\/ML has greatly enhanced the metabolomics field, enabling biomarker identification, disease prediction, and classification of metabolic patterns at an unprecedented level. This study analyses and compares the scientific goals, methodologies, datasets, and sources of AI\/ML approaches applied to metabolomic data, as well as assessing their implications in precision medicine. We systematically reviewed recent advancements in AI\/ML applications to metabolomic data, focusing on peer-reviewed research indexed in PubMed. Significant number of studies were analyzed, covering diseases such as cancer, cardiovascular diseases, and diabetes. Our results showed that the most used AI\/ML techniques were SVM, RF, Gradient Boosting, and Logistic Regression, highlighting their effectiveness in processing complex metabolic data. Despite these advancements, key challenges persist in AI\/ML applications to metabolomics data, including small cohort sizes, data heterogeneity, and the need for improved model interpretability, and these challenges must be considered for future use. Ultimately, our findings underscore the transformative potential of AI\/ML in metabolomics and its critical role in advancing precision medicine by uncovering novel metabolic pathways, improving treatment strategies, and enabling the earlier diagnosis of diseases through predictive metabolic profiling.<\/p>\n

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