| Autor: |
Theodorakis N; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece.; School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, Greece., Feretzakis G; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Tzelves L; 2nd Department of Urology, Sismanoglio General Hospital, Sismanogliou 37, National and Kapodistrian University of Athens, 15126 Athens, Greece., Paxinou E; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Hitas C; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece., Vamvakou G; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece., Verykios VS; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Nikolaou M; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece. |
| Abstrakt: |
Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy. |
