Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning

Abstract

Convolutional neural networks accurately classify closely related cellular phenotypes based on actin organization and cell shape. Interpretable machine learning reveals features of the global actin structure relevant for classification tasks.