Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assay.

Abstract

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 γH2AX 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.