Received: 20.02.2024/Accepted: 20.03.2024/ Published online: 30.03.2024
УДК 631.362.36:57.087.3
DOI: 10.26212/2227-1937.2024.50.73.018
M. Suleimenova1, ORCID: https://orcid.org/0009-0003-8553-5353
Ch. Siming2, ORCID: https://orcid.org/0000-0002-2690-3588
A. Shomanov3, ORCID: https://orcid.org/0000-0001-8253-7474
K. Abzaliyev1, ORCID: https://orcid.org/0000-0003-2452-854X
A. Kurmanova1, ORCID: https://orcid.org/0000-0002-1859-3903
S. Abzaliyeva4, ORCID: https://orcid.org/0009-0004-9802-0129
M. Abdykassymova1, ORCID: https://orcid.org/0000-0002-5365-5508
U. Sagalbayeva1, ORCID: https://orcid.org/0000-0001-7903-9116
R. Bitemirova1, ORCID: https://orcid.org/0000-0002-5228-6018
D. Sundetova1, ORCID: https://orcid.org/0000-0003-2343-869X
A. Bugibayeva1, ORCID: https://orcid.org/0000-0003-1417-2711
1Al-Farabi Kazakh National University, Almaty, Kazakhstan
2Fudan University, Shanghai, China
3Nazarbayev University, Astana, Kazakhstan
4Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
AI IN IDENTIFYING AGING MARKERS
Resume. As the global population ages, there is a growing need to understand the complex processes associated with aging
and to identify reliable markers that can aid in early diagnosis, intervention, and personalized healthcare. This article
provides a comprehensive review of the applications of Artificial Intelligence (AI) in identifying aging markers. The
integration of AI techniques, such as machine learning and data analytics, has significantly advanced our ability to analyze
vast and diverse datasets related to genomics, proteomics, metabolomics, imaging, and clinical records. The review discusses
the integration of clinical data, lifestyle factors, and environmental information using AI, providing a holistic understanding of
aging markers. The investigation explores the use of AI in predicting an individual’s risk of accelerated aging by considering
diverse factors.
The integration of AI into the identification of aging markers represents a paradigm shift in aging research. This review
underscores the potential of AI in revolutionizing our understanding of aging and paving the way for innovative strategies in
age-related disease prevention and management.
Key words: Artificial Intelligence, aging biomarkers, machine learning, deep learning.
