机器学习方法捕捉细胞衰老异质性的前景。

IF 17 Q1 CELL BIOLOGY
Imanol Duran, Cleo L. Bishop, Jesús Gil, Ryan Wallis
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引用次数: 0

摘要

由于衰老细胞固有的异质性和缺乏通用标记,识别衰老细胞是一项长期悬而未决的挑战。在这篇评论中,我们将讨论最近出现的基于机器学习的方法,通过使用无偏见的多参数形态学评估来识别衰老细胞,以及这些工具如何帮助未来的衰老研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The promise of machine learning approaches to capture cellular senescence heterogeneity

The promise of machine learning approaches to capture cellular senescence heterogeneity

The promise of machine learning approaches to capture cellular senescence heterogeneity
The identification of senescent cells is a long-standing unresolved challenge, owing to their intrinsic heterogeneity and the lack of universal markers. In this Comment, we discuss the recent advent of machine-learning-based approaches to identifying senescent cells by using unbiased, multiparameter morphological assessments, and how these tools can assist future senescence research.
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CiteScore
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