A nuclear morphology-based machine learning algorithm for senescence detection

IF 81.3 1区生物学 Q1 CELL BIOLOGY

Nature Reviews Molecular Cell Biology Pub Date : 2024-10-23 DOI:10.1038/s41580-024-00796-y

Imanol Duran

引用次数: 0

Abstract

In this Tools of the Trade article, Duran (Gil lab) describes the development of novel machine learning algorithms that enable the detection of senescent cells in vitro and in diverse tissues based solely on nuclear morphologeny analysis.

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基于核形态学的衰老检测机器学习算法

在这篇《贸易工具》（Tools of the Trade）文章中，Duran（Gil 实验室）介绍了新型机器学习算法的开发情况，该算法能够仅根据核形态学分析检测体外和不同组织中的衰老细胞。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Reviews Molecular Cell Biology 生物-细胞生物学

CiteScore

173.60

自引率

0.50%

发文量

118

审稿时长

6-12 weeks

期刊介绍： Nature Reviews Molecular Cell Biology is a prestigious journal that aims to be the primary source of reviews and commentaries for the scientific communities it serves. The journal strives to publish articles that are authoritative, accessible, and enriched with easily understandable figures, tables, and other display items. The goal is to provide an unparalleled service to authors, referees, and readers, and the journal works diligently to maximize the usefulness and impact of each article. Nature Reviews Molecular Cell Biology publishes a variety of article types, including Reviews, Perspectives, Comments, and Research Highlights, all of which are relevant to molecular and cell biologists. The journal's broad scope ensures that the articles it publishes reach the widest possible audience.