.png){kind=icon}
返回
Measuring biological age using a functionally interpretable multi-tissue RNA clock
.png){kind=icon}
OA
AI
DOI：10.1111/acel.13799.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The quantification of the biological age of cells yields great promises for accelerating the discovery of novel rejuvenation strategies. Here, we present MultiTIMER, the first multi-tissue aging clock that measures the biological, rather than chronological, age of cells from their transcriptional profiles by evaluating key cellular processes. We applied MultiTIMER to more than 70,000 transcriptional profiles and demonstrate that it accurately responds to cellular stressors and known interventions while informing about dysregulated cellular functions.
Keyword:
aging
machine learning
transcriptomics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
3.7K
被引数:
1.9W
机构
引用论文
A Fixed-Point Neural Network Architecture for Speech Applications on Resource Constrained Hardware用于资源受限硬件上语音应用的定点神经网络体系结构
PREAI
分享收藏Identification and Functional Characterization of Hypoxia-Induced Endoplasmic Reticulum Stress Regulating lncRNA (HypERlnc) in Pericytes.png)
CIRCULATION RESEARCHOAAI
CIRCULATION RESEARCHIF16.2
OAAI分享收藏FoxM1 repression during human aging leads to mitotic decline and aneuploidy-driven full senescenceNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Hematopoietic Stem Cell Quiescence Promotes Error-Prone DNA Repair and MutagenesisCELL STEM CELLOAAI
CELL STEM CELLIF20.4
OAAI分享收藏
查看更多内容