"Cell images contain a vast amount of quantifiable information about the status of the cell: for example, whether it is diseased, whether it is responding to a drug treatment, or whether its function has been disrupted by a genetic mutation. We aim to go beyond measuring individual cell features that biologists already know are relevant to a particular disease. Instead, in a strategy called image-based profiling, often using the Cell Painting assay, we extract hundreds of features of cells from images. Just like transcriptional profiling, the similarities and differences in the patterns of extracted features reveal connections among diseases, drugs, and genes and are a rich source for machine learning.

We are harvesting similarities in image-based profiles to identify how diseases, drugs, and genes affect cells, which can uncover the impact of drugs and genes, predict assay outcomes, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles, and find novel therapeutic candidates."