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Jing-Dong J. Han on Muck Rack

Jing-Dong J. Han

As seen in: Nature, Wiley Online Library, ScienceDirect, Science Magazine, Cell Metabolism, Cell Press
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Articles

A generative AI framework unifies human multi-omics to model aging, metabolic health, and intervention response

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By Jiawei Chen, Ya Ren, Yong Zhou, Ziyang Wang, Kehang Mao, Zhengqing Yu, Jiyang Li, Xiaoxiao Guo, Hao Xu, Yiyang Wang, Yi Wang, Hongxiao Liu, Huiru Tang, Jing-Dong J. Han
| Cell Metabolism Verified
AURORA-generated data enable more accurate disease prediction than real data models in China cohorts We also assessed the ability of cAgeDiffs to reflect biological health and disease states. We tested whether cAgeDiffs were associated with age-related chronic diseases, including fatty liver disease, liver cysts, hypertension, hyperlipidemia, type 2 diabetes (T2D), and cardiovascular disease (CVD).
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Deep learning models identify brain changes during the progression of Alzheimer’s disease - npj Systems Biology and Applications

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By Jinhui Sun, Jing-Dong J. Han, Weiyang Chen
| Nature Verified
Abstract Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder whose progression is closely associated with time. However, most diagnostic models are based on single time-point data, overlooking longitudinal disease characteristics. Structural magnetic resonance imaging (sMRI) has been widely utilized in the study of AD.
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Lifespan‐Extending Endogenous Metabolites

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By Yizhou Jiang, Hainan Academy, Jing-Dong J. Han
| Wiley Online Library Verified
1 Introduction Aging is a multifactorial process driven by the interplay of genetic, environmental, and metabolic perturbations, with dysregulated nutrient sensing and metabolic dysfunction recognized as core hallmarks (Lopez-Otin et al. 2023). Interventions that alter metabolite availability or metabolism, such as caloric restriction (CR) or reduced insulin/IGF-1 signaling (IIS), extend lifespan across species (Gao et al. 2018).
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