利用深度学习神经网络准确识别复杂临床前模型中的癌细胞：无转染方法（生物学进展 11/2024）

IF 3.2 3区生物学 Q3 MATERIALS SCIENCE, BIOMATERIALS

Advanced biology Pub Date : 2024-11-12 DOI:10.1002/adbi.202470112

Marilisa Cortesi, Dongli Liu, Elyse Powell, Ellen Barlow, Kristina Warton, Caroline E. Ford

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引用次数: 0

摘要

准确识别癌细胞区分共培养物中不同类型细胞的贡献是一项重大挑战。Marilisa Cortesi、Caroline E. Ford 及其合作者通过一种基于深度学习的软件工具解决了这一难题。这种方法为在共培养中使用多种细胞类型（包括源自患者的细胞）提供了可能性。更多详情，请参阅文章编号 2400034。图片由 Tim Salita 博士创建。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach (Adv. Biology 11/2024)

查看原文本刊更多论文

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach (Adv. Biology 11/2024)

Accurate Identification of Cancer Cells

Distinguishing the contribution of different cell types in co-cultures is a major challenge. Marilisa Cortesi, Caroline E. Ford, and co-workers have addressed it through a deep learning-based software tool that distinguishes healthy and cancer cells solely from the shape of the nucleus. This method opens to the possibility of using a wide variety of cell types, including patient-derived ones, in co-cultures. More details can be found in article number 2400034. Image created by Dr. Tim Salita.

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来源期刊

Advanced biology Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)

CiteScore

6.60

自引率

0.00%

发文量

130