Middle East Technical University, Northern Cyprus Campus, Electrical and Electronics Engineering Program, Kalkanli, Turkey.
Middle East Technical University, Northern Cyprus Campus, Computer Engineering Program, Kalkanli, Turkey.
J Biomed Opt. 2024 Aug;29(8):080502. doi: 10.1117/1.JBO.29.8.080502. Epub 2024 Aug 28.
Azimuth-resolved optical scattering signals obtained from cell nuclei are sensitive to changes in their internal refractive index profile. These two-dimensional signals can therefore offer significant insights into chromatin organization.
We aim to determine whether two-dimensional scattering signals can be used in an inverse scheme to extract the spatial correlation length and extent of subnuclear refractive index fluctuations to provide quantitative information on chromatin distribution.
Since an analytical formulation that links azimuth-resolved signals to and is not feasible, we set out to assess the potential of machine learning to predict these parameters via a data-driven approach. We carry out a convolutional neural network (CNN)-based regression analysis on 198 numerically computed signals for nuclear models constructed with varying in steps of between 0.4 and , and varying in steps of 0.005 between 0.005 and 0.035. We quantify the performance of our analysis using a five-fold cross-validation technique.
The results show agreement between the true and predicted values for both and , with mean absolute percent errors of 8.5% and 13.5%, respectively. These errors are smaller than the minimum percent increment between successive values for respective parameters characterizing the constructed models and thus signify an extremely good prediction performance over the range of interest.
Our results reveal that CNN-based regression can be a powerful approach for exploiting the information content of two-dimensional optical scattering signals and hence monitoring chromatin organization in a quantitative manner.
从细胞核中获得的角分辨光散射信号对其内部折射率分布的变化敏感。因此，这些二维信号可以为染色质组织提供重要的见解。
我们旨在确定二维散射信号是否可以在逆方案中使用，以提取亚核折射率波动的空间相关长度 和 ，从而提供关于染色质分布的定量信息。
由于将角分辨信号与 和 联系起来的解析公式不可行，我们着手评估机器学习通过数据驱动方法预测这些参数的潜力。我们对用 0.4 到 之间以步长 变化构建的核模型进行了 198 个数值计算信号的卷积神经网络 (CNN) 回归分析， 以步长 0.005 从 0.005 到 0.035 变化。我们使用五重交叉验证技术来量化我们分析的性能。
结果显示， 和 的真实值和预测值之间存在一致性，平均绝对百分比误差分别为 8.5%和 13.5%。这些误差小于各自参数的最小递增百分比，这些参数分别表示构建模型的特征，因此在感兴趣的范围内表示出非常好的预测性能。
我们的结果表明，基于 CNN 的回归可以成为利用二维光散射信号信息量并以定量方式监测染色质组织的强大方法。
