基于卷积神经网络的回归分析,用于从二维光学散射信号中预测亚核色素组织
Yazdan Al-Kurdi1, Cem Direkoǧlu1, Meryem Erbilek2
1Middle East Technical University, Northern Cyprus Campus, Electrical and Electronics Engineering Program, Kalkanli, Turkey.
Journal of biomedical optics
|August 29, 2024
概括
机器学习使用光学散射数据准确预测染色质组织. 卷积神经网络提取亚核折射率的细节,从而可以对染色质分布进行定量监测.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 细胞生物学 细胞生物学
背景情况:
- 来自细胞核的阿齐木斯分辨光学散射信号对内部折射率配置文件敏感.
- 这些信号提供了对染色质组织和亚核折射率波动的见解.
研究的目的:
- 为了确定二维散射信号是否可以量化提取空间相关性,以及亚核折射率波动的长度和程度.
- 通过反向方案提供有关染色质分布的定量信息.
主要方法:
- 一种使用机器学习 (卷积神经网络 - CNN) 进行回归分析的数据驱动方法.
- 对具有不同参数的核模型进行198个散射信号的数值计算.
- 五倍交叉验证以量化预测性能.
主要成果:
- 基于CNN的回归准确地预测了折射率波动的空间相关性长度 () 和范围 ().
- 平均绝对百分比错误为8.5%的数学和13.5%的数学.
- 预测性能很好,错误小于模型参数增量.
结论:
- 基于CNN的回归是一种分析二维光学散射信号的强大方法.
- 这种方法可以对染色体组织进行定量监测.
- 这项研究表明了机器学习在生物物理分析中的潜力.
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