AI’s contemporary capabilities allow it to learn from existing data, predicting and modeling properties crucial for unraveling the complexities of aging. In their recent review paper, Marino et al. spotlight a pivotal shift towards AI-driven longevity research, moving beyond traditional structural data storage methods.
- Expanding Data Horizons in Modern Aging Technologies: Marino et al. highlight that modern technologies, tapping into diverse information sources like next-generation sequencing data (proteomics, lipidomics, and other omics), provide a comprehensive understanding of the interactions between the human body and external aging influences.
- Crucial Role of External Factors in Aging: External factors’ key role in aging gains prominence, especially with AI shedding light on complex biological processes.
- AI’s Promise as a Cornerstone in Aging Research? With advancements in computational systems biology and ongoing biomarker development, AI emerges as a central ally in the pursuit of understanding and addressing the intricacies of aging.
Photo Credits: Marino et al. Front. Aging (2023)