About this Research Topic
Aging is an inevitable part of life, yet it remains one of the most complex and least understood processes. Research into the biology of aging could lead to interventions that extend human lifespan and uncover new ways to prevent, delay, or even reverse age-related conditions. In recent decades, the importance of gene expression leakage and epigenomic remodeling has come to the forefront of this research. Consequently, gene-related studies play a crucial role in the quest to cure and treat aging-oriented diseases. Among various approaches, the intersection of artificial intelligence and biomedical research has opened new avenues for understanding and combating age-related diseases. Deep learning algorithms, particularly those leveraging multi-omics data and large models, have shown significant promise in aging-related research. However, despite these advancements, there remains a significant gap in translating these findings into practical interventions and personalized strategies for healthy aging. The integration of AI with genetic research is still in its nascent stages, and more comprehensive studies are needed to fully harness its potential.
This research topic aims to use genetic research to help understand the fundamental mechanisms that drive aging at the cellular and molecular levels, prevent diseases that afflict older adults, including chronic diseases, cognitive decline, and physical frailty, and develop personalized strategies for healthy aging. By addressing these objectives, the research seeks to answer critical questions about the genetic underpinnings of aging and the role of AI in developing effective interventions. Hypotheses will be tested regarding the impact of gene expression and epigenomic changes on aging, as well as the efficacy of AI-driven approaches in predicting and mitigating age-related conditions.
To gather further insights in the intersection of genetic mechanisms and AI integration in aging research, we welcome articles addressing, but not limited to, the following themes:
• Genetic Mechanisms of Aging: Exploring the role of gene expression leakage and epigenomic remodeling in driving the aging process at both cellular and molecular levels.
• Disease Prevention in Older Adults: Conducting research to prevent age-related conditions, including chronic diseases, cognitive decline, and physical frailty.
• AI and Genetic Research in Aging: Applying artificial intelligence, particularly deep learning algorithms that integrate multi-omics data, to gain insights into and address age-related diseases.
• Personalized Aging Strategies: Creating tailored interventions and strategies that promote healthy aging based on comprehensive genetic and multi-omics data analysis.
This research topic aims to use genetic research to help understand the fundamental mechanisms that drive aging at the cellular and molecular levels, prevent diseases that afflict older adults, including chronic diseases, cognitive decline, and physical frailty, and develop personalized strategies for healthy aging. By addressing these objectives, the research seeks to answer critical questions about the genetic underpinnings of aging and the role of AI in developing effective interventions. Hypotheses will be tested regarding the impact of gene expression and epigenomic changes on aging, as well as the efficacy of AI-driven approaches in predicting and mitigating age-related conditions.
To gather further insights in the intersection of genetic mechanisms and AI integration in aging research, we welcome articles addressing, but not limited to, the following themes:
• Genetic Mechanisms of Aging: Exploring the role of gene expression leakage and epigenomic remodeling in driving the aging process at both cellular and molecular levels.
• Disease Prevention in Older Adults: Conducting research to prevent age-related conditions, including chronic diseases, cognitive decline, and physical frailty.
• AI and Genetic Research in Aging: Applying artificial intelligence, particularly deep learning algorithms that integrate multi-omics data, to gain insights into and address age-related diseases.
• Personalized Aging Strategies: Creating tailored interventions and strategies that promote healthy aging based on comprehensive genetic and multi-omics data analysis.
Keywords: Machine/Deep learning, Aging, Multi-omics, Biomarker discovery, Risk factors
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
