Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assay
Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assay
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DOI：
10.1371/journal.pone.0229620
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发表时间：
2020-02-26
期刊：
影响因子：
3.7
通讯作者：
Dehghani, Faramarz
中科院分区：
文献类型：
--
作者：
Hohmann, Tim;Kessler, Jacqueline;Dehghani, Faramarz
除其他外，电离辐射会引起最严重的DNA损伤：双链断裂(DSB)。这种损伤的有效修复对细胞生存和基因组稳定至关重要。DSB相关的病灶分析通常是手动或用自动系统进行的。人工评估是耗时和主观的，而大多数自动化方法容易受到实验条件的变化或图像伪影的影响。本文研究了多种机器学习模型，即多层感知器分类器(MLP)、线性支持向量机分类器(SVM)、互补朴素贝叶斯分类器(CNB)和随机森林分类器(RF)，以正确地分类包含多种伪影的人工标记图像中的Gamma H2 AX焦点。所有模型都与训练图像的人工评分有较好的一致性(Matthews相关系数为0.4)。然后，在不同实验条件下得到的图像上应用性能最好的模型。因此，MLP模型产生了最好的结果，F1得分为0.9。因此，我们已经证明，所使用的方法足以模拟手动计数，并且对图像伪影和实验条件的变化具有健壮性。
Ionizing radiation induces amongst other the most critical type of DNA damage: double-strand breaks (DSBs). Efficient repair of such damage is crucial for cell survival and genomic stability. The analysis of DSB associated foci assays is often performed manually or with automatic systems. Manual evaluation is time consuming and subjective, while most automatic approaches are prone to changes in experimental conditions or to image artefacts. Here, we examined multiple machine learning models, namely a multi-layer perceptron classifier (MLP), linear support vector machine classifier (SVM), complement naive bayes classifier (cNB) and random forest classifier (RF), to correctly classify gamma H2AX foci in manually labeled images containing multiple types of artefacts. All models yielded reasonable agreements to the manual rating on the training images (Matthews correlation coefficient > 0.4). Afterwards, the best performing models were applied on images obtained under different experimental conditions. Thereby, the MLP model produced the best results with an F1 Score > 0.9. As a consequence, we have demonstrated that the used approach is sufficient to mimic manual counting and is robust against image artefacts and changes in experimental conditions.