CELL-E: A Text-To-Image Transformer for Protein Localization Prediction

Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer architecture that generates a 2D probability density map of protein distribution within cells. Given a amino acid sequence and a reference image for cell or nucleus morphology, CELL-E offers a more direct representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.