![]() ![]() We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. ![]() Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. ![]()
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