基于深度强化学习的多组学整合构建gtAge——源自IgG N-糖组与血液转录组的新型衰老时钟

Yao Xia; Syed Mohammed Shamsul Islam; Xingang Li; Abdul Baten; Xuerui Tan; Wei Wang

doi:10.1016/j.eng.2025.08.016

工程（英文） ›› 2026, Vol. 57 ›› Issue (2) : 100 -112. DOI: 10.1016/j.eng.2025.08.016

研究论文

基于深度强化学习的多组学整合构建gtAge——源自IgG N-糖组与血液转录组的新型衰老时钟

Yao Xia ^a^,^#^,^* ,
Syed Mohammed Shamsul Islam ^a^,ⁱ^,^# ,
Xingang Li ^b^,^# ,
Abdul Baten ^c^,^d ,
Xuerui Tan ^e ,
Wei Wang ^b^,^e^,^f^,^g^,^h^,^*

作者信息 +

Deep Reinforcement Learning-Driven Multi-Omics Integration for Constructing gtAge: A Novel Aging Clock from the IgG N-Glycome and Blood Transcriptome

Yao Xia ^a^,^#^,^* ,
Syed Mohammed Shamsul Islam ^a^,ⁱ^,^# ,
Xingang Li ^b^,^# ,
Abdul Baten ^c^,^d ,
Xuerui Tan ^e ,
Wei Wang ^b^,^e^,^f^,^g^,^h^,^*

Author information +

^a School of Science, Edith Cowan University, Joondalup, WA 6027, Australia

^b Nutrition and Health Innovation Research Institute & School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia

^c Institute of Precision Medicine and Bioinformatics, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia

^d Department of Biomedical Informatics and Digital Health, School of Medical Sciences, University of Sydney, Sydney, NSW 2050, Australia

^e Clinical Research Centre, The First Affiliated Hospital & Institute for Glycome Study, Shantou University Medical College, Shantou 515041, China

^f Chemistry and Chemical Engineering Guangdong Laboratory, Shantou 515041, China

^g Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 110069, China

^h School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an 271016, China

ⁱ Department of Computing and Information System, Daffodil International University, Dhaka 1216, Bangladesh

^j Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6009, Australia

^*E-mail addresses: yao.xia@uwa.edu.au (Y. Xia),

wei.wang@ecu.edu.au (W. Wang).

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Abstract

Previous studies have demonstrated that the immunoglobulin G (IgG) N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages, and several aging clocks have been developed. By integrating the IgG N-glycome and transcriptome, we propose a novel aging clock, gtAge. We developed a deep reinforcement learning-based multiomics integration method called AlphaSnake. The results showed that AlphaSnake achieved a predicted coefficient of determination (R²) value of 0.853, outperforming the concatenation-based integration method (R² = 0.820). The gtAge estimated by AlphaSnake explained up to 85.3% of the variance in chronological age, which was higher than that in age predicted from IgG N-glycome solely (gAge; R² = 0.290) and age predicted from transcriptome solely (tAge; R² = 0.812). We also found that the delta age—the difference between the predicted age and chronological age—was associated with several age-related phenotypes. Both delta gtAge and tAge were negatively associated with high-density lipoprotein (p = 0.02 and p = 0.022, respectively), whereas delta gAge was positively correlated with cholesterol (p = 0.006), triglyceride (p = 0.002), fasting plasma glucose (p = 0.014), low-density lipoprotein (p = 0.006), and glycated hemoglobin (p = 0.039). These findings suggest that gtAge, tAge, and gAge are potential biomarkers for biological age.

关键词

Key words

Aging clock / IgG N-glycome / Transcriptome / Multiomics integration / Deep reinforcement learning / Biological age

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Yao Xia,Syed Mohammed Shamsul Islam,Xingang Li,Abdul Baten,Xuerui Tan,Wei Wang. 基于深度强化学习的多组学整合构建gtAge——源自IgG N-糖组与血液转录组的新型衰老时钟[J]. 工程（英文）, 2026, 57(2): 100-112 DOI:10.1016/j.eng.2025.08.016