Philip John Harrison

The paper combines molecular and cell painting image data to predict drug mechanisms of action, showing improved performance with integrated data.

This study compares the predictive performance of models trained on fluorescence and brightfield images for drug mechanism of action prediction, finding comparable results.

The paper introduces the HASTE Toolkit, a cloud-native toolkit for intelligent data pipelines that prioritize resource usage based on data content, demonstrating it in microscopy imaging case studies.

The paper generates fluorescent labels from bright-field images using deep learning to enable virtual staining of adipocyte cells, improving feature extraction for downstream analysis.

This study uses transfer learning with pre-trained deep convolutional neural networks to classify cellular morphological changes from drug-treated cells, achieving 95-97% accuracy.