Created
Jun 1, 2023 4:32 AM
Discipline
Machine LearningTransformersMultimodal
Date
June 2, 2022
Journal
Nature Portfolio
Year
2022
Abstract
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 model that generates 2D probability density images describing the spatial distribution of proteins within cells. Given an amino acid sequence and a reference image for cell or nucleus morphology, CELL-E predicts a more refined 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.