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Updated: Feb 9, 2026

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基于可解释的深度学习的非侵入性瘤监测和诊断协议
Zhenbo Yuan1, Yuli Yan1, Youpeng Yang1
1School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
STAR protocols
|February 7, 2026
概括
这项研究引入了一种使用深度学习来追踪瘤DNA甲基化血液变化的新方法. 这种非侵入性方法通过分析无细胞DNA (cfDNA) 来监测癌症治疗反应.
科学领域:
- 生物化学 生物化学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 非侵入性瘤检测对于早期诊断和监测至关重要.
- 无等离子细胞DNA (cfDNA) 甲基化模式有可能成为癌症生物标志物.
- 治疗反应的动态监测需要敏感和特定的分析工具.
研究的目的:
- 提出一种分析瘤特异性DNA甲基化在血cfDNA中的协议.
- 使用可解释的深度学习框架 (Oncoder) 来监测治疗反应.
- 为了使瘤甲基化信号的动态变化的非侵入性跟踪.
主要方法:
- 开发用于血cfDNA甲基化分析的协议.
- 差异甲基化分析以识别瘤特异信号.
- 对Oncoder深度学习模型的培训和解释.
- 数据准备和模型验证步骤.
主要成果:
- 在cfDNA中进行瘤特异性DNA甲基化概况的详细协议.
- 通过甲基化动态来监测Oncoder治疗反应的能力的演示.
- 该协议可以适应各种数据类型和研究场景.
结论:
- 本方案为非侵入性癌症监测提供了一种可靠的方法.
- 编码器提供了一个可解释的深度学习解决方案,用于分析cfDNA甲基化.
- 这种方法通过液体活检促进了对治疗疗效的动态评估.
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