{"title":"Synergizing metabolomics and artificial intelligence for advancing precision oncology.","authors":"Yipeng Xu, Xiaojuan Jiang, Zeping Hu","doi":"10.1016/j.molmed.2025.01.016","DOIUrl":null,"url":null,"abstract":"<p><p>Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.</p>","PeriodicalId":23263,"journal":{"name":"Trends in molecular medicine","volume":" ","pages":""},"PeriodicalIF":12.8000,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in molecular medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.molmed.2025.01.016","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.
期刊介绍：
Trends in Molecular Medicine (TMM) aims to offer concise and contextualized perspectives on the latest research advancing biomedical science toward better diagnosis, treatment, and prevention of human diseases. It focuses on research at the intersection of basic biology and clinical research, covering new concepts in human biology and pathology with clear implications for diagnostics and therapy. TMM reviews bridge the gap between bench and bedside, discussing research from preclinical studies to patient-enrolled trials. The major themes include disease mechanisms, tools and technologies, diagnostics, and therapeutics, with a preference for articles relevant to multiple themes. TMM serves as a platform for discussion, pushing traditional boundaries and fostering collaboration between scientists and clinicians. The journal seeks to publish provocative and authoritative articles that are also accessible to a broad audience, inspiring new directions in molecular medicine to enhance human health.
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