With the aging problem intensifying, anti-aging research has become an important topic in the field of biomedicine, in which biomarker identification and validation is a key link in revealing the aging mechanism and assessing the effect of anti-aging interventions Traditional biomarker identification methods have problems of low efficiency and lack of precision, and it is difficult to cope with large-scale and multi-dimensional biological data. In recent years, the development of artificial intelligence technology has provided new solutions for anti-aging biomarker research. By introducing machine learning and deep learning algorithms, not only can potential anti-aging biomarkers be efficiently mined from multi-omics data, but also accurate data-driven validation models can be constructed to enhance the reliability and clinical translation potential of biomarkers. This paper reviews the current status of AI applications in anti-aging biomarker identification and validation, focuses on the methods and case studies of machine learning and deep learning techniques in marker screening and validation, and assesses the challenges and future development directions in this field. By systematically sorting out the current progress, this paper aims to provide theoretical support and practical reference for further application of AI technology in anti-aging research.

Zikuan Bai. Artificial intelligence-assisted identification and validation of anti-aging biomarkers. Academic Journal of Medicine & Health Sciences (2024), Vol. 5, Issue 12: 39-45. https://doi.org/10.25236/AJMHS.2024.051207.

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