Abstract
We present a new, carefully designed and well-annotated dataset of images and image-based profiles of cells that have been treated with chemical compounds and genetic perturbations. Each gene that is perturbed is a known target of at least two compounds in the dataset. The dataset can thus serve as a benchmark to evaluate methods for predicting similarities between compounds and between genes and compounds, measuring the effect size of a perturbation, and more generally, learning effective representations for measuring cellular state from microscopy images. Advancements in these applications can accelerate the development of new medicines.
Competing Interest Statement
AEC has optional ownership interest in Recursion, a public biotechnology company using image-based profiling for drug discovery. SES is an employee of Dewpoint Therapeutics. Daniel Kuhn is an employee of Merck Healthcare KGaA, Darmstadt, Germany.
Footnotes
↵^ co-senior author
https://github.com/jump-cellpainting/2021_Chandrasekaran_submitted
