Predicting senescence across tissues and species using deep learning

被引：0

作者：

Heckenbach, Indra ^{[1
]}

Scheibye-Knudsen, Morten ^{[1
]}

机构：

[1] Univ Copenhagen, Copenhagen, Denmark

来源：

NATURE AGING | 2022年 / 2卷 / 08期

关键词：

D O I：

10.1038/s43587-022-00265-1

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Deep learning was applied to cellular images to predict senescence on the basis of nuclear morphology. These methods recognize senescence in diverse cell types, show increasing senescence with age in liver and dermis, and suggest that higher rates of senescence associate with several age-related diseases but reduced cancer risk.

引用

页码：688 / 689

页数：2

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