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eXplainable Artificial Intelligence (XAI) in aging clock models
DOI:10.1016/j.arr.2023.102144.png)
摘要
En 中文
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the aging clocks and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
Keyword:
Explainable artificial intelligence
Aging
Longevity
Age-related diseases
Machine learning
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:
12.4
论文数:
3.6K
被引数:
2.3W
机构
引用论文
Interference Screw vs. Suture Anchor Fixation for Open Subpectoral Biceps Tenodesis: Does it Matter?
Environmental influences on cardiovascular variables in rainbow trout, Oncorhynchus mykiss (Walbaum)
A pan-tissue DNA-methylation epigenetic clock based on deep learning基于深度学习的泛组织DNA甲基化表观遗传时钟
NPJ AGING
IF6
Artificial intelligence for aging and longevity research: Recent advances and perspectives人工智能老龄化与长寿研究: 最新进展与展望
AGEING RESEARCH REVIEWS
IF12.4

