{"id":1338,"date":"2024-03-30T08:25:20","date_gmt":"2024-03-30T08:25:20","guid":{"rendered":"https:\/\/journal.nncf.kz\/?p=1338"},"modified":"2024-04-22T08:26:28","modified_gmt":"2024-04-22T08:26:28","slug":"ai-in-identifying-aging-markers","status":"publish","type":"post","link":"https:\/\/journal.nncf.kz\/kz\/ai-in-identifying-aging-markers\/","title":{"rendered":"AI IN IDENTIFYING AGING MARKERS"},"content":{"rendered":"

Received: 20.02.2024\/Accepted: 20.03.2024\/ Published online: 30.03.2024
\n\u0423\u0414\u041a 631.362.36:57.087.3
\nDOI: 10.26212\/2227-1937.2024.50.73.018<\/a><\/p>\n

M. Suleimenova1, ORCID: https:\/\/orcid.org\/0009-0003-8553-5353
\nCh. Siming2, ORCID: https:\/\/orcid.org\/0000-0002-2690-3588
\nA. Shomanov3, ORCID: https:\/\/orcid.org\/0000-0001-8253-7474
\nK. Abzaliyev1, ORCID: https:\/\/orcid.org\/0000-0003-2452-854X
\nA. Kurmanova1, ORCID: https:\/\/orcid.org\/0000-0002-1859-3903
\nS. Abzaliyeva4, ORCID: https:\/\/orcid.org\/0009-0004-9802-0129
\nM. Abdykassymova1, ORCID: https:\/\/orcid.org\/0000-0002-5365-5508
\nU. Sagalbayeva1, ORCID: https:\/\/orcid.org\/0000-0001-7903-9116
\nR. Bitemirova1, ORCID: https:\/\/orcid.org\/0000-0002-5228-6018
\nD. Sundetova1, ORCID: https:\/\/orcid.org\/0000-0003-2343-869X
\nA. Bugibayeva1, ORCID: https:\/\/orcid.org\/0000-0003-1417-2711
\n1Al-Farabi Kazakh National University, Almaty, Kazakhstan
\n2Fudan University, Shanghai, China
\n3Nazarbayev University, Astana, Kazakhstan
\n4Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan<\/p>\n

AI IN IDENTIFYING AGING MARKERS<\/p>\n

Resume. As the global population ages, there is a growing need to understand the complex processes associated with aging
\nand to identify reliable markers that can aid in early diagnosis, intervention, and personalized healthcare. This article
\nprovides a comprehensive review of the applications of Artificial Intelligence (AI) in identifying aging markers. The
\nintegration of AI techniques, such as machine learning and data analytics, has significantly advanced our ability to analyze
\nvast and diverse datasets related to genomics, proteomics, metabolomics, imaging, and clinical records. The review discusses
\nthe integration of clinical data, lifestyle factors, and environmental information using AI, providing a holistic understanding of
\naging markers. The investigation explores the use of AI in predicting an individual’s risk of accelerated aging by considering
\ndiverse factors.
\nThe integration of AI into the identification of aging markers represents a paradigm shift in aging research. This review
\nunderscores the potential of AI in revolutionizing our understanding of aging and paving the way for innovative strategies in
\nage-related disease prevention and management.
\nKey words: Artificial Intelligence, aging biomarkers, machine learning, deep learning.<\/p>\n

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