Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

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作者： Zhi-Peng,Li ^[1] ; Zhaozhen,Du ^[2] ; De-Shuang,Huang ^[3] ; Andrew E,Teschendorff ^[4]

作者单位： Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, 315201, Zhejiang, China.;School of life sciences, University of Science and Technology of China, Hefei, 230026, Anhui, China. ^[1] CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China. ^[2] Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, 315201, Zhejiang, China. dshuang@tongji.edu.cn. ^[3] CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China. andrew@sinh.ac.cn. ^[4]

关键词 AI Aging Deep-learning Epigenetics Explainable AI Graph-level representation learning Single-cell

主题词衰老(Aging);人类(Humans);单细胞分析(Single-Cell Analysis);后成说, 遗传(Epigenesis, Genetic);DNA甲基化(DNA Methylation);人工智能(Artificial Intelligence)

DOI 10.1038/s41598-025-89646-1

PMID 39934290

发布时间 2025-02-13

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