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.
The research aims to use genetic research to help understand the fundamental mechanisms that drive aging at the cellular and molecular levels, prevent disease that afflict older adults, including chronic diseases, cognitive decline, and physical frailty, and develop personalized strategies for healthy aging.
The research topics includes but not limited to:
• 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.
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.
The research aims to use genetic research to help understand the fundamental mechanisms that drive aging at the cellular and molecular levels, prevent disease that afflict older adults, including chronic diseases, cognitive decline, and physical frailty, and develop personalized strategies for healthy aging.
The research topics includes but not limited to:
• 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.