CELL-E: Biological zero-shot text-to-image synthesis for protein localization prediction

Khwaja, E.; Song, Y. S.; Huang, B.

doi:10.1101/2022.05.27.493774

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bioRxiv · 10.1101/2022.05.27.493774

CELL-E: Biological zero-shot text-to-image synthesis for protein localization prediction

Khwaja, E.·Song, Y. S.·Huang, B.

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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 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.

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Khwaja, E., Song, Y. S., Huang, B.. 2022-05-29. CELL-E: Biological zero-shot text-to-image synthesis for protein localization prediction. https://doi.org/10.1101/2022.05.27.493774

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